{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABU量化系统使用文档 \n",
    "\n",
    "<center>\n",
    "        <img src=\"./image/abu_logo.png\" alt=\"\" style=\"vertical-align:middle;padding:10px 20px;\"><font size=\"6\" color=\"black\"><b>第9节 港股市场的回测</b></font>\n",
    "</center>\n",
    "\n",
    "-----------------\n",
    "\n",
    "作者: 阿布\n",
    "\n",
    "阿布量化版权所有 未经允许 禁止转载\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[本节ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)\n",
    "\n",
    "上一节讲解的是A股市场的回测，本节讲解港股市场的回测示例。\n",
    "\n",
    "首先导入abupy中本节使用的模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "# 基础库导入\n",
    "\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境\n",
    "abupy.env.enable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "买入因子，卖出因子等依然使用相同的设置，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuFactorAtrNStop, AbuFactorPreAtrNStop, AbuFactorCloseAtrNStop, AbuFactorBuyBreak, ABuProgress\n",
    "from abupy import abu, tl, get_price, ABuSymbolPd, EMarketTargetType, AbuMetricsBase, AbuHkUnit, six\n",
    "\n",
    "# 设置初始资金数\n",
    "read_cash = 1000000\n",
    "\n",
    "# 买入因子依然延用向上突破因子\n",
    "buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak},\n",
    "               {'xd': 42, 'class': AbuFactorBuyBreak}]\n",
    "\n",
    "# 卖出因子继续使用上一节使用的因子\n",
    "sell_factors = [\n",
    "    {'stop_loss_n': 1.0, 'stop_win_n': 3.0,\n",
    "     'class': AbuFactorAtrNStop},\n",
    "    {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},\n",
    "    {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 港股市场的回测示例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "择时股票池使用沙盒缓存数据中的如下股票：\n",
    "\n",
    "#### 港股市场：\n",
    "\n",
    "* 中国恒大(hk03333)\n",
    "* 腾讯控股(hk00700)\n",
    "* 长城汽车(hk02333)\n",
    "* 中国信达(hk01359)\n",
    "* 复星国际(hk00656)\n",
    "* 金山软件(hk03888)\n",
    "* 中国平安(hk02318)\n",
    "* 恒生指数(hkHSI)\n",
    "\n",
    "代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 择时股票池\n",
    "choice_symbols = ['hk03333', 'hk00700', 'hk02333', 'hk01359', 'hk00656', 'hk03888', 'hk02318']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与A股市场类似首先将abupy量化环境设置为港股，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 设置市场类型为港股\n",
    "abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_HK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   n_folds=6,\n",
    "                                                   choice_symbols=choice_symbols)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:202\n",
      "买入后尚未卖出的交易数量:3\n",
      "胜率:50.4950%\n",
      "平均获利期望:12.9899%\n",
      "平均亏损期望:-5.9130%\n",
      "盈亏比:2.4770\n",
      "策略收益: 85.8217%\n",
      "基准收益: 18.5928%\n",
      "策略年化收益: 14.6923%\n",
      "基准年化收益: 3.1830%\n",
      "策略买入成交比例:90.7317%\n",
      "策略资金利用率比例:32.5890%\n",
      "策略共执行1472个交易日\n"
     ]
    },
    {
     "data": {
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mZE7oVz8URIMSGxEREWnXFS3McgRd0Y7EacNOjlTc2ly9hXWVm5iePTkq9+pJ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\nh31uWW1PL53thxto6nBGeEH3lbbwTq90j1abm4qGTmXbNUANyBu7Kvn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rAVG3uMlYLsJI4ca6bb7cVmVXhl3WHz8eE2iV0UENicGtC3QYhIkeSfWHrkJb2f1bP3nQVgXiQG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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b72f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x10e5c12b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上收益曲线可以看到在大概在2015年上旬的有一次收益猛烈的拉升出现，下面输出top10笔收益，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>buy_date</th>\n",
       "      <th>buy_price</th>\n",
       "      <th>sell_date</th>\n",
       "      <th>sell_price</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-17</th>\n",
       "      <td>hk03333</td>\n",
       "      <td>20150317</td>\n",
       "      <td>3.4450</td>\n",
       "      <td>20150422</td>\n",
       "      <td>5.1250</td>\n",
       "      <td>97440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-03</th>\n",
       "      <td>hk03333</td>\n",
       "      <td>20150303</td>\n",
       "      <td>3.4600</td>\n",
       "      <td>20150422</td>\n",
       "      <td>5.1250</td>\n",
       "      <td>94905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-10</th>\n",
       "      <td>hk00656</td>\n",
       "      <td>20150310</td>\n",
       "      <td>12.7400</td>\n",
       "      <td>20150422</td>\n",
       "      <td>18.6000</td>\n",
       "      <td>90830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-27</th>\n",
       "      <td>hk02318</td>\n",
       "      <td>20141127</td>\n",
       "      <td>32.3125</td>\n",
       "      <td>20150116</td>\n",
       "      <td>43.6500</td>\n",
       "      <td>68025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-30</th>\n",
       "      <td>hk02333</td>\n",
       "      <td>20120730</td>\n",
       "      <td>8.7200</td>\n",
       "      <td>20121122</td>\n",
       "      <td>12.4875</td>\n",
       "      <td>67815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-22</th>\n",
       "      <td>hk03888</td>\n",
       "      <td>20120822</td>\n",
       "      <td>3.6150</td>\n",
       "      <td>20121015</td>\n",
       "      <td>4.9000</td>\n",
       "      <td>65535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>hk00700</td>\n",
       "      <td>20120131</td>\n",
       "      <td>37.6000</td>\n",
       "      <td>20120419</td>\n",
       "      <td>47.6200</td>\n",
       "      <td>53106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-05</th>\n",
       "      <td>hk03888</td>\n",
       "      <td>20130805</td>\n",
       "      <td>15.0700</td>\n",
       "      <td>20131021</td>\n",
       "      <td>22.2500</td>\n",
       "      <td>50260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-05</th>\n",
       "      <td>hk03888</td>\n",
       "      <td>20130805</td>\n",
       "      <td>15.0700</td>\n",
       "      <td>20131021</td>\n",
       "      <td>22.2500</td>\n",
       "      <td>50260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-13</th>\n",
       "      <td>hk00656</td>\n",
       "      <td>20121213</td>\n",
       "      <td>4.6150</td>\n",
       "      <td>20130121</td>\n",
       "      <td>5.6550</td>\n",
       "      <td>44720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             symbol  buy_date  buy_price  sell_date  sell_price   profit\n",
       "2015-03-17  hk03333  20150317     3.4450   20150422      5.1250  97440.0\n",
       "2015-03-03  hk03333  20150303     3.4600   20150422      5.1250  94905.0\n",
       "2015-03-10  hk00656  20150310    12.7400   20150422     18.6000  90830.0\n",
       "2014-11-27  hk02318  20141127    32.3125   20150116     43.6500  68025.0\n",
       "2012-07-30  hk02333  20120730     8.7200   20121122     12.4875  67815.0\n",
       "2012-08-22  hk03888  20120822     3.6150   20121015      4.9000  65535.0\n",
       "2012-01-31  hk00700  20120131    37.6000   20120419     47.6200  53106.0\n",
       "2013-08-05  hk03888  20130805    15.0700   20131021     22.2500  50260.0\n",
       "2013-08-05  hk03888  20130805    15.0700   20131021     22.2500  50260.0\n",
       "2012-12-13  hk00656  20121213     4.6150   20130121      5.6550  44720.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_pd = abu_result_tuple.orders_pd\n",
    "top_10 = orders_pd.sort_values(by='profit')[::-1].dropna(subset=['sell_price'])[:10]\n",
    "top_10.filter(['symbol', 'buy_date', 'buy_price', 'sell_date', 'sell_price', 'profit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "从输出可以清晰的看出，top3的高收益交易都发生在2015年3月份买入，4月份卖出，3笔收益都非常高。\n",
    "\n",
    "结合HSI基准在3-4月之间也有一个拉升，这里的收益曲线猛烈拉升也可以理解了。\n",
    "\n",
    "在《量化交易之路》中写过：很多时候我们编写出一个策略后发现预期收益非常高，遇到这样的情况，如果你是认真的，那就应该考虑怎么降低收益，降低的不仅是收益也是风险，对应的提高的将是系统的稳定性。\n",
    "\n",
    "有很多朋友问怎样才能降低风险，提高稳定性，比如上面的回测，是不是要把3-4月份这次收益猛烈的拉升滤除才叫降低风险呢？\n",
    "\n",
    "当然不是，上面的3-4月份拉升收益是顺势的收益，如果要把这段过滤将是逆势行为。\n",
    "\n",
    "有些朋友问降低风险是不是需要通过买对冲来实现？\n",
    "\n",
    "其实也不建议个人投资者买对冲，因为对冲的产品一般都是和时间相关的，比如期权，期货，增加一个时间维度会将整个交易复杂度提升几个数量级，并不建议一般交易者进行这种对冲，特别是以股票为主要投资对象的一般交易者（但是可以在策略中做相同品种的相关性对冲，在之后的章节会有完成相关性策略示例）。\n",
    "\n",
    "我认为最适合一般投资者的的提高稳定性的方法就是降低交易频率，比如在《量化交易之路》主要讲解的ump裁判拦截，主要就是通过学习策略中的失败交易，从不同角度进行学习交易特点，指导决策拦截交易，拦截了大量的交易可以节省佣金，更重大的意义在于边裁避免了重大的风险。\n",
    "\n",
    "本节讲解在不考虑使用ump系统的情况下，示例如何通过硬编码策略来降低交易频率，提高系统的稳定性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2. 优化策略，提高系统的稳定性\n",
    "\n",
    "下面的示例将在突破策略AbuFactorBuyBreak基础上进行降低交易频率，提高系统的稳定性处理。\n",
    "\n",
    "首先看下面使用AbuTLine.show_least_valid_poly示例，通过get_price获取港股大盘HSI一个月收盘价格数据，然后使用AbuTLine.show_least_valid_poly进行poly判断，它返回的结果是检测至少poly次拟合曲线可以代表原始曲线y的走势，如下所示显示1次\n",
    "曲线即可以代表20160301-20160401这一个月的拟合走势，如下所示:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TNjQU6+q1vh47OblojZdWsTjU0MeE20vBooVT7Tlu/TJf1eft0mNcvzIFS+j8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\nlPP+PyIMGbZkAoFsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11efc3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "metrics_func rolling_mean=186.61431257386164, metrics_func y_fit=183.13826895414556\n",
      "least poly = 1, zoom=False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsi = get_price('hkHSI', start_date='20160301', end_date='20160401')\n",
    "tl.AbuTLine(hsi.price, 'HSI').show_least_valid_poly()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "验证poly（默认＝1）次多项式拟合回归的趋势曲线是否能代表原始曲线y的走势主要代码以及思路如下所示：\n",
    "\n",
    "    def valid_poly(y, poly=1, zoom=False, show=True, metrics_func=metrics_rmse):\n",
    "        \"\"\"\n",
    "        验证poly（默认＝1）次多项式拟合回归的趋势曲线是否能代表原始曲线y的走势，\n",
    "        基础思路：\n",
    "                 1. 对原始曲线y进行窗口均线计算，窗口的大小＝ math.ceil(len(y) / 4)\n",
    "                 eg：\n",
    "                    原始y序列＝504 －> rolling_window = math.ceil(len(y) / 4) = 126\n",
    "                 2. 通过pd_rolling_mean计算出均线的值y_roll_mean\n",
    "                 3  使用metrics_func方法度量原始y值和均线y_roll_mean的距离distance_mean\n",
    "                 3. 通过计算regress_xy_polynomial计算多项式拟合回归的趋势曲线y_fit\n",
    "                 4. 使用metrics_func方法度量原始y值和拟合回归的趋势曲线y_fit的距离distance_fit\n",
    "                 5. 如果distance_fit <= distance_mean即代表拟合曲线可以代表原始曲线y的走势\n",
    "        :param y: 原始可迭代序列\n",
    "        :param poly: 几次拟合参数，int\n",
    "        :param zoom: 是否对y数据进行缩放\n",
    "        :param show: 是否原始曲线y，均线，以及拟合曲线可视化\n",
    "        :param metrics_func: 度量始y值和均线y_roll_mean的距离和原始y值和\n",
    "                             拟合回归的趋势曲线y_fit的距离的方法，默认使用metrics_rmse\n",
    "        :return: 是否poly次拟合曲线可以代表原始曲线y的走势\n",
    "        \"\"\"\n",
    "        valid = False\n",
    "        x = np.arange(0, len(y))\n",
    "        if zoom:\n",
    "            # 将y值 zoom到与x一个级别，不可用ABuScalerUtil.scaler_xy, 因为不管x > y还y > x都拿 x.max() / y.max()\n",
    "            zoom_factor = x.max() / y.max()\n",
    "            y = zoom_factor * y\n",
    "        # 对原始曲线y进行窗口均线计算，窗口的大小＝ math.ceil(len(y) / 4)\n",
    "        rolling_window = math.ceil(len(y) / 4)\n",
    "        # 通过pd_rolling_mean计算出均线的值y_roll_mean\n",
    "        y_roll_mean = pd_rolling_mean(y, window=rolling_window, min_periods=1)\n",
    "        # 使用metrics_func方法度量原始y值和均线y_roll_mean的距离distance_mean\n",
    "        distance_mean = metrics_func(y, y_roll_mean, show=False)\n",
    "\n",
    "        # 通过计算regress_xy_polynomial计算多项式拟合回归的趋势曲线y_fit, 外面做zoom了所以zoom=False\n",
    "        y_fit = regress_xy_polynomial(x, y, poly=poly, zoom=False, show=False)\n",
    "        # 使用metrics_func方法度量原始y值和拟合回归的趋势曲线y_fit的距离distance_fit\n",
    "        distance_fit = metrics_func(y, y_fit, show=False)\n",
    "        # 如果distance_fit <= distance_mean即代表拟合曲线可以代表原始曲线y的走势\n",
    "        if distance_fit <= distance_mean:\n",
    "            valid = True\n",
    "        if show:\n",
    "            # 原始曲线y，均线，以及拟合曲线可视化\n",
    "            plt.plot(x, y)\n",
    "            plt.plot(x, y_roll_mean)\n",
    "            plt.plot(x, y_fit)\n",
    "            plt.legend(['close', 'rolling window={}'.format(rolling_window), 'y_fit poly={}'.format(poly)])\n",
    "            plt.show()\n",
    "            log_func('metrics_func rolling_mean={}, metrics_func y_fit={}'.format(distance_mean, distance_fit))\n",
    "        return valid\n",
    "        \n",
    "具体更多详情请阅读源代码AbuTLine\n",
    "\n",
    "如上20160301-20160401使用一次拟合曲线即可以代表原始曲线，下面向前推一个月20160128－20160229："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9OrJbFM44cx4z55hR+zjv4aWHPmOnpiyXyL1tzKzsw986NCObw0OLT8YcfBcu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\n2ew50MGqeZFcd07qlH9g6OV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SVtNO4d4WAo2eRIdO3ENcbYM2CptLGHQMYvSYvA+L9YyN\nQxtkwpq1A2vmDrzi49EGjfw+qPHu63aHg+c/smC3Oxi0O9hba2XF7DBUJ3hRejRS+DjZSHbgXZYG\nskubOHluBImRR2+bcjgc/Hv3SzT3tnJdyhWEeh//XqAT0UgPGipPTwwLFuIRGUV3SQldeTl05mSj\ni44Z0RWagQE7ueVN+PvojvnvdqIY68HaT+fL4tD5RBrCqeqooaillK37d6BSFGYYIuQBqMcgJ+Du\nIXk/utyyZr7ZXceq2eHMiv3hoxl0HmrS4gJZkhpGr22AospWMosbyK9oJsjXC5Of5xELIHfkPKuk\ngfWbSwny9eSeK+agn+Kzlw329ND87gbq//UMtv01eMbFE3bbnfifctpR73cdyYXEpBn+bC04QP6e\nFhYlhzj1QuCgfZCSljI+2PcJ60veILMum637d9Dd30OcbzQa1eT8N9FFzcAjIgJrVibWzO3oZkTj\nERI6onXHu6/vrbWyOauaRSkhRIcaKNzbgpeHhoRImW3uSKTwcbKR7MCbvqlkf1MXV5ySeMxpP7Mb\n8vm0+ivSgpI5K/ZUZ4c6bYz2oKELC8d3+XLsXd10F+bTsfUrBnt68EqcecxnEgT6evJxZjXdff2s\nnC3tbuM5WCuKQqh3MMsiFuGj9aaibS8FzUVk1udg9DAQ5h1ywrdQHImcgLuH5P3oNn69l/1NXVx1\n2kz8fI5+/6neU8OcRBMLk4OxdvdTtK+Vb3bXUVrdRkiA/getOa7O+d7aDp54Kx+NRsXPrpiDyW/q\nzuDmsNvp2PLV0H08RYVo/PwIvua6oft4Ao59b+VI8q731OBv0JFV3EBVnZUlqWHjOl47HA6qrDV8\nUvkl60veYOuBHezvrMVPZ2RJ+EI6+7vY3VxCVl0OJn0gIfrJOaucLjwCz9g4rDszse7YjkdoGLqI\n458rjHdf/2RnNeX72zl/WSzL08PYml9L0b5WTpoVit5zchaK7iSFj5MdbwcetNt54SMLRm8PLlwZ\nd9SDRd+gjafzn2fA3s8t6T/CWzv2qRKnu7EcNFRaD3wyZuNlThp6WFt+Hh2Z29FFRKI1Hfmg6qFV\nU1bTRmlNO0vTQtGf4O1uzjgxUSkqYn1nsDR8IXaHHUtrOdkN+RS1lBLmHYy/p4ysHUpOwN1D8n5k\nfbZBnvuohCBfz2P+PjuUQe/BgqRgZicE0WrtY/e+Vrbk11JZZyU8yBvf4YuBrsx5q7WPR18ZmsHt\n9gvSpvQENt0lxdQ+9cRwO7eDwHPOI+zHtxx2H8+xjDTvkSYf9jd1Ubi3BZ2HekxdEE09zXxZs42X\nLW+xufIL9nVUoVU0LA6bz8WJ53JhwlpSAs0sDVuIoigUtZSSVZ9DbVc98b4xk3KiJ4/gELwSZ9K5\nKwvrju1oAgLwnBF9zHXGs687HA5e3FzKwKCDa88wo/fUYtB7sNPSQFN7D4tSQsa03elMCh8nO94O\nXFbdxhe5B1g8K4SMhKPPvPHRvk8oaC7m1OhVzAvJmIhQp43xHDS0QUH4Ll8JDgddBfl0fPM1qFR4\nJRz52Ur9A3byypvxN3ie8MPIzjwx0aq1JAfOZEHoHNptVkpaSvmmNovazjqiDJFS+A+TE3D3kLwf\nWXZpIzuKGjh5biTJMaObpdHPR8fiWaEkR/vT0NrN7n2tfJmzn7qWbqJMPgQH+bgk5339g/z5tVzq\nWnq4dHUCy9LDJvwzJ4KtoYH65/5N09tvMNjRgXHJ0qH7eDJmH7OT4ftGuq8rikJytD/bCusoqGhm\ndqLpYNF6LFZbJ9trd/Jm6UY2VGyitK0C26CNOaY0zos/k8vNF5BumkWAp9/B38FqlZqZ/glkmFLZ\nP/x8uG21meg1XkQaxv6MvomiDQxCn5KKNXsnnZk7UOn1eMXFH3X58Rxfahq72PRNJXNmmlg8a6i1\nLirYB0tVG4V7ZerxI5HCx8mOtwN/nr3/4JBkSMCRT+aaelp4tugVDFofbky9etL2tE4W4z0pUdRq\n9MkpeKem0VVYSFdONn1VlXinpaHSHn4gDxh+mGlv3wArMqbPFKdjMREng3qtnrnB6ST5J1LbVU9J\naxlb9m+ne6CHGGMUWrWMsskJuOtJ3o/s7a/2UNfSzTWnzzxm2/axBPp6sjQtjPgIX/Y3dVG0r5XP\ns/fT3N5DeIAerwm8z8bucPDP94oo2tfKsrQwLl4VP+lOoo9nsLub5nfepv7f/8R2YD+eCYmE33Yn\nfiefOqbn1o1mX9dp1YQF6vlmdz3lNW0sSw9HfYQpy22DNnIaC3i34gNeLd1AYXMx7X0dJPknckbs\nqVydfAkLQ+cSojcdcwIng4cPi8PmY/QwUNIy9HiE0tZyYn1n4OMxtmfoTBSNnx/e6Rl0ZmfTuStr\n6IJq4kyn38/2eXYNluo2zl363aNRFEUhLtzIl7kHKK1pY0VG+JiepTVdSeHjZMfbgV/9tIw+2yDX\nrC7DqEAAACAASURBVDEfdbaY9SVvUNtVxxXmC4k2HnvWN+G8kxKNvz/GxUvoq6qiu7CAzl270Ccl\nozEaDy6j06oprR5qd1ueHjahv5Qnu4k8GQzw9GNJ2AJCvIPZ11FNUYuFbQcy0ag0RBnCT9jZDeUE\n3D0k7z/U3TvACx9bCAvSc96yuHFtS1GU/8/eecdHVWZ9/HunJzOT3jtpk5AQIITQRVEUpAjqil3X\nta3u7uv2XV+765bX7brrurp2144oihSl1xAgIXUS0nvvZTLlvn8EEJGSMpOZJPf7+fD5AJl57pkn\nd577nOec8zsEertzyYwQQv11VDV2k13czPajNfT0m4kM0qNW2l/05JO9Zew4VkN8mCf3r0keVwpu\nos1Gx+5d1P3z7/Tm56Hw9ibw9u/if8ONKL1H3iNtuPd6kI877d0mckpbsVptJE0ZvLbVZsXYeoIv\nTooUZDZk0djXTJg+hCsiFnNr4ndYHDafcH0IymEc7gqCQKRHOHOCU2ntb6OgtYh9tRnYRBtTPCOR\nu9CzQaH3QJeaSnf2MXqOHkEcMOE+Nelbzs9o1pe3vyyiz2TljmUJKBVff3a9uwqLdTBDxWK1kXwO\n4ZHJiuT42JkL3cDNHX2s311K0hQfFkw7dzi9oLWIjaWbifaM4rq4VePu9MkZ2HNTIlOr0c+dh2ix\n0JN9jM79e1H6+6MO/doBHTBbOV7Sgo+HhpjQyZvu5ujNoCAIhOiCWBQyF41CQ3F7Kceb8zjakI23\nxpNAd/9J9/2QNuDOQZr3b5NR0MARYxNXzArDEDG8viXnQxAEQv20XDozhOgwL4oqWskta2XHsRos\nFhuRQfpvbO5GQ0ZBA29vK8bPU8NPx5mCW29BPrX/fI7OvbtBEAbreO65H01ExKjXxJHc6wkRXhwu\naOR4STM+gSYOtx44KVJw8LRIweLQ+dyUcC3Loi4n2jMSjWJ0fWY0Cg2zAqcTpgvhRHspOS0FHGs8\nTog2GF83+9yP9kCu1aJLTaM35zg92VlYOzvQTkv5xu9ppOtLfWsvG/aUkRLje84UzZgQTw4VNJBb\n2saMOD88LyA+MpmQHB87c6Eb+GB+A8dLWrhydjhTgj2+9XOrzcqLx1+nx9zLfSl34Kn+9mskvo29\nNyWCIKCdmoQqNIzurGN0ZxzE2teHe0IigkyGj4eGrRmV9JutE6qj93AZq82gXCYnxiuK+SGzGbCa\nKWwrJrMhi6L2EkK0QXipJ4/zKW3AnYM0799m/e5SGtr6uGNZAjo3+6agygSBafEBpBv80bsrKa3p\n4HhpK7uzaxEEgYhA3aiiM4MKbjmoxpmC20BDPfWv/YeWjz86WcezkNAf/AhdygwEuX0iYiO519sH\n2uh0L6LW7RB5vRmnRQrmBM86LVJg8IlzSDpakDaA+SHpmKwD5LcYOVifSYepgxjPKJdJjZa7uaFL\nS6c3P4+e49mYGxsGa69O9hAc6fqyK6uGgoo2rp4bSUTgt/scyeUygnzcOZBXT1VjNwtTRqe+N1GQ\nHB87c6Eb+JO9ZTS09XHblYZzSgzurNrL4YZjLAydy4KQOY42dcLgqE2JOiQEfWoqvQX59BzPoq/I\niDY5BXe9lsKKNoqrO7hkesikTXcb682gWq4i2S+B1IAU2kxtFLYWs682g6beFiI9wnAb5QnieEDa\ngDsHad6/SXefmTe3GIkI0LNifpRDrqHVqunvNxMT4smlM0MH04yrO8g+0czenDrUSjlhATpk56gp\nuRBtXSb+751j9Jks/ODaaeOiJ5u1t4eWj9dT/8pLmOtqcYuLJ+SBH+J12RJkGvs6bUO917sHejhY\nl8kHJ0UKqvsqkCttmFsCiVfM4eHL7mT6WSIFjkIpU5Dkm0Cij4HyzkryWwcdIG+1l8u0RpCp1ejT\n0+krKqI35zim6ip0M1MR5PIRry/vbS+ms8fMnVcnoDpPKmigtzu1J9X3vHRqos5x6D7ZkBwfO3O+\nG9hktvLmFiPBvu6smBf1rZ93DnTxUs5bqORK7p12Byqpi/2QceSmRK7X4zF/AQMNDfTm5tCZcRC3\nmFisOk9ySlvw89QQHTJ5Ig5n4qzNoE6lJS1wJrGeU6jprqOwrYg9NQcYsJqJ9Aib0GIg0gbcOUjz\n/k0O5jdwrLiZK9PDHeY4nDnnCrmM+HAvFs8YVPAyVrZztLiZQ/kN6NyUhPhrh7S5NZmt/OndLBra\n+li3JPa8Keeugmi10rF7J3X/eJ7egnwUvr4E3v5d/L6zbkhNt0fChe71r0UKvuDdovWnRQoM3rEs\nn3IFNxuuJydTQ3GJlaggjzFXE/PWeLIgJB2VTEVBaxFHGrOp7Kom2jMKd6Xzo3oypQp9+hz6y8ro\nzT1Of2kJutRZ6Dy1w15fWjr6eX9HCYlR3lw688K9guLCPNmdXUtBeRsLUoLRqCZ3k3DJ8bEz51s0\ncstaOZBbz8KUYJLOIfv5QdGnlHdWsjZ2JfHe55c9lPg2Dq81USjRpc1GplbTc+wonfv3ERoewI56\nGSaLbdzKn44WZ28G/dx8WBCSjq+bD2UdFeS1FnK0IZs5Qakuk+Jgb5w955MVad6/yfs7TtDc0c+d\nyxMc1s/sXHOuUsqZGuXDwpRgzBYbBRVtZBqbOFrUhJdeTZCP+3kdIJso8tKneRRUtLEoJZjrFru2\ngltPft5gHc++PSDI8LtmDUF334smfPR1PBfi7Hm3iTYK24r5ouwskQJdMJdHXHJSpGAB4foQVAol\ncWGe7DleR35ZK/OnBTtElOJCyAQZMV5TSA2YTn1Pw6D4QV0GKrmSSI9wp//OBYUC3ezZmGqq6c3N\noddYSMCC+fRbxGGNsy+njtyyVpbNiTxn6cSZaFQKNCoFR4ua6Og2McsQMJqPMO6RHB87c74H5NbD\nVZTXd3H94hh8Pb+ZklPeWcl7RRsI0QZxc8J1k1axaqSMxaZEEATcYuNwi4unJzsbU/YRwlUD7O/1\n5JKZYWhUEzfKcD5cYTMoCALh+hAWhs6lz9JHQVsxrf1tzPCf5vQHnCNwhTmfjEjz/jUdPQO8va2I\nmFAPlqVfuDHjaLjQnGtUClJi/JiXFERfv4X8ijYO5TeSV9aKv5fbOWt2NuwpY2dWLfHhXnx/TTJy\nmWs+Zwfq66l/9WVaNqzH2t2Fx8JLCP3BDwcL4u1Ux3MhtFo1PT0mqrpq+LJyF28VfMCemkGRAk+1\nB5ecFClYPuUKoj2jviVS4KFVoVLKOFrUTENrL+mJAU5Zi7VKd9KDUvFz88XYdoLspjzyWgqI9Ah3\nev20IJejT03D3NREb85x2o9l4T4jFZl66OIDH+w4QWuniTuXJwxp/xEVpOd4SQu5Za3EhnkSME7q\n2hyB5PjYmXMt1qIo8tZWIzJB4OalccjOWARsoo2Xc9+i3dTBXcm34O8uSQ4Ol7HclCj9/QdD1SdO\noKsuJqanmjrvcKKiJl93ZFfaDCpkchJ94jG2lZDfasTXzYcw/cQTnnClOZ9MSPP+Nfty6jhe0sKy\n9AiHqloOZc61GiWp8f6kJQTQ2T1AXnkb+3PrKa5uJ8hHi7d+cCN5ML+ed74sxt9Lw89unOGSdZnW\nnh6aP/6I+ldfxlxXh1u8gZAHf4jX4suQacamfrG5r5Xd1ft5I/cDtlRsp6yzErkgZ07QLK6LW821\nsStI8IlDfxGRgugQD4qqBhto+nm6nbPwfiwQBIEwfQhzg9PoHOgiv7WI/XWHMVlNRHtGoZA5L+VL\nkMnQzUzF2tlBV1YW3dnH0M1IHVLvpY6eAd75spjYME+uSAsf2vUEgaggD3Zn11JS08HiGSEu6/w7\nGsnxsTPnWqxrmnrYdLCS1Hh/Zid8c4N8qP4Iu2v2kxqQwtLIS8fQ0onDWG9K5G7u6OfNp7elHU2F\nEXX+UbRRkagCJpfz40qbwT6ThQ92lqLo9adNWUJeSwEzA1LQKs/dJHi84kpzPpmQ5v1r3tteTFun\niTuvTnSoAzGcOfdwV5GeGEhKjC8tnf3kl7exO7uWqsZubDaRVzYVolbJ+NlNqfh5utZJt2i10rFr\nB7X/fI6+wgKUvn4E3vFd/K6/AaWX44UXTNYBjjRk8WHxp3x0YiN5jUWYrCam+yezOnoZNyVcy3T/\nZHw03kOO3AiCgCHCi705deSUtjAnMdBhKZFDQS1XMcM/mWjPSEray8htKSSzIYtAd3/83f2cZpcg\nCGhTpuOmgPbMI3QfzUQ7fSZy7YVrow7m1ZNd0sKVaeHDOnzw0qnp6TeTU9qKTBBIiHQd2e+xRHJ8\n7My5Fus9x2tPSw6GB3x9UtJn6efFnNcQRZH7U+7ETeFaC/J4wRmbEkEuxzM1lc2FnYQ0l9Jz6AAI\nwnk7M09EXGUzWFzdzp/eyyKvrJWqOhPrFk3jeEsO5Z2VzA1Km1Cpo64y55MNad4Hae3s592vTpAQ\n4TXkk+aRMpI599armZ8chCHci/q2XvLL2zhS1ISIyINrU4gLcy0hmp68XGr/8Xc69+9DkMnwvWYt\nQXffgybMsXU8oihS3lk52Fy04H2ONGbT2t9GnFc066at5IaYtcwJmkWQNmDE66e7RomXTs3hwkYq\nGrqZnxzk9Gejv5svC0LSsYk28luNZNQfpam3mRivKaidJCglCAIh82fT22+h59hRujIPo02e9o3G\n6Wfz0e5Smtr7uH2ZYdgOZWyoJwfy6skrayUtIQC9++QT0pIcHztzrsX6o10ltHWZuGPZNyUHN5Zu\nprC1mKujriDFP2msTZ0wOHNTUqf0ZmODhmSxCdPxLEyVFWinTUOmnPiLibM3gxarjQ17S3l1UyF9\nJguh/lq6es1clpiATNNHfqsRURQx+MQ6zUZ74+w5n6xI8z7I7uxa8spaWT734gXVo2U0c+7n5cai\nlGCigj3o7Blgxbwo0hNdJyI/0FBP/Ssv0fLJx1i7u/FYdAkhD/4QbbJj63g6B7rYU3OQ/xZ+yNbK\nnVR11aBT6bg0fCG3JtzA5RGXkBQWy0C/zS7XCw/QUd00KKWsUSmIdQHHUy6Tk+ATR4rfVKq6ashv\nNXKg9jA6lY4wnXP63Gi1asSwKcjctXQfOUzX4UO4J05F4fXtiExPv5m3thYREajn6rnDr7FTKmT4\nemg4VNBIXUuvSzikY835HB/XS4Adp3T3mTlR00FMiOc3mrzV9zSyo2ovvhofrohY7EQLJUZDmiGA\n97b78WX0DVzbtI+e7Cwqf/MUIQ/8AHWYY09EJzN1LT28tDGf8vou/Dw13L1yKr0mC3//8DjldV2s\nm7uG0o5ytlTsIMEnnjjvaGebLCEx7skoaEQmCMwy+DvblIsiCAIzYv2YEeu8VKazEUWRzj27aXz3\nbcSBAdwMCfivuwlNhONEIqw2K3kthRyoyyS3pQCbaEMhyEkNSGFe8GwSfOIcFhUXBIHblxk4UdPB\n+t0lJE/xISzA/k1MR0KYPoSfpT3Irur9fFq6mbcK3iej7gg3JVxLgLtz7m/vK5Yi02hoeP0Vqv/4\nB0J+9GPc4w3feE1WcTNWm0jaKL6Dswz+pMT4crykhYN5DcxLDhqt6ROCiZMb4mRyy1oQRUiJ+Vq0\nQBRFPiz+FJto47q4lRNWency4OupYUqwB8frTHjc90N8rl6JubGByt8+Teehg842b8IhiiI7jlbz\n5KuHKa/vYsG0IJ68K534cC+iggYLaMvru3BTaLgz6UYEQeD1/HfpNfc52XIJifFNY3sfZXWdJEZ5\n4zEJ02NGi7W7m7oXnqfhjVcRFAqC7r2fsJ/90mFOT31PIx+f+JxH9v+WF3Ne53hzHsHaQL4Tdw3P\nLHyE7yXfylRfg8NTgT3cVdy5PAGLVeSlz/IxW+wTTbIHMkHGZeELeXTOT0n2TaSovYRnMv7C5vKv\nsNgsTrHJc+Eigu/7PjazmZq//ome3OPf+PkRYxMAqfEjd3wEQeDWpfGoFDLe3V5Md595VDZPFKRU\ntxFydnh+08EKqpt6WLckFk/doMJMTnM+myu2k+Adx6roZZMuzGhvnJ2G0msyk1fWSpCvlsTL5qAK\nC6cn6xhdGQex9vbgnjAVYQKqp4z1vHd0m/jXp3lsy6xGo5Jzz8qprJwfhVIxOLcalYLd2bW0dZtY\nlh6Bt8YLEZGc5vwJI3Ht7Ht9siLNO+w8VkNBRRsr50cSOQYqXRNpznsLC6j5yx/pLyvFLd5A2I9/\njrsD6kH7Lf1k1B/lPeMGPin9gtKOCuSCnPkh6dxkuJaV0VcS5RlxwQbpjpj3IB932rr6ySltxWYT\nmXqOXobOxE3hRlrgDIJ1QRS3lZDTnM/xpjzC9CF4axwvMHH2nKtDQlFHRtF9OIPOQwdRhYSgDgml\nf8DC65uNBPu6s3rhlFFd012jRCYTyCpuptdkcanIqKORUt0ciM0mklvairdefVrUwGw181HxRmSC\njO/Erx73GzGJwXS3D3aUkFnYyCXTQ9CnzkIdEkLtP56j/cttmCoqCL7/ARSejl9AJypHi5p47YtC\nuvvMJE3x4a6rE09L1Z5JZKCerBPNtHWZ8NarWRa5hMLWYo40ZjPV18Dc4DQnWC8hMf7JKGhELhNG\nddI82RAtFlo+3UDrF5+DIOC75lp8rl5p14MwURQp6SjnQO1hjjZmM2AzIyCQ6BPPvOA0UvySXCKr\nZN2SOAoq2vjiUAXTY32JC3Ot56EgCKQGpJDgHccnJZvYW3uIPx/5J4tC57I6ZtmYi0/pUqYT+tBP\nqfn7X6n71z+x3fk9jD5xWKy2b3wHRVHEZDXROdBFr6WPUG3wkH/fV84O50BuPbuyalmQHOwSNVjO\nRIr4jJAzPfeSmk6+OlrNnKmBzIgbvFG3Ve4iqymHy8IXkh6U6kxTJwzOPhnUapRknWimtLaTJalh\nqJRy5Do9HvPnM9DQQG9uDp2HDuIWE4PSZ+L0aRqLee8fsPDmFiMf7ixBBG5cEsvNS+NxP4+MbkNb\nL4WV7SREeBPk645MkGHwjuFAXeaEkLh29r0+WZns817X0sPHe8qYHuPHopSx6Y813ud8oKGBmr//\nhe7DGSj9/Qn90Y/xmDvPboed7aYOdlXv563CD/iqajfV3bV4qT25PPwSbpt6A4vDFhCiC0I+zF41\njpp3pUJGVJCevTl1FFa2sXBa8OlovSuhlCuZ5jcVg3csZR0V5LUaOVR3FD83H4K0AQ655rnm3Gw1\n06NTYYoOxZadR8/hDHJ7q6gK6UAVWMO++gNsKd/Op6Wb+aL8K3ZV72d/bQb76jKwiTZCdMEoZReO\nYchkAuEBOvYer6OsrpNF00OQySb+YbwU8XEg2SXNwNf1PW397Wwp/wq9UsfVU65wpmkSdibN4E9F\nfRdZxc0sTAkGQKZxI/j+B2nbupnmD9+n6tnfE7DuJjwvu1yK9A2BEzUdvLwxn8b2PiICdNyzOolQ\nvwv3N4gKGlSaKq/vZEbcYOje182HGw1reS3/HV7Lf4efpj4w7M2AhMRkJqOgEYDZiY7Z+E0kRFGk\nc/8+Gv/7FqKpH/28+QTcfNuQGlNeDIvNQk5zAQfqDpPfYkRERClTMDswlfkhacR6Rbu0fH9cmBfL\n50Sy6WAF720/wZ3LE5xt0nmJ9ZrCr9IfYlvFDraUb+ffOW8w3T+ZG+KvwUs9ssiI1Waly9xN50AX\nXQPddJq66BzowlxlorGjlc6BrtN/+iz9p9/ne5k7a7cPkJZtxDRdS2agFpkgQ6/UEagNQK/S4aHS\nIxdkHGnI5pOSL9hasYNFofO4LHwhHqrzp6bGhXlxyfRgdmfXsS2ziuVzHCe04epIjo8dOF7SgkIu\nY2rkYD7rhpJNDNjMfCd+jdSzZ4KRlhDAR7tKyTQ2nnZ8YDB87nPVcjSRUdS9+E8a//sWfWWlBN56\nBzL1t1O1JAZlqjfuK+ezA+UgwtVzI1mzaAoK+cUf6GcKHJzJ7KCZ5LUUcrjhGJvKtrEqZpkDLJeQ\nmHiIokhGQQNKhWxS1QGMBGtvD41vvk7X4Qxkbm4E3nMfHnPmjXrcmu46DtQd5nD9MbrNPQBE6sOZ\nFzKbWQHTcVeOn/3EmkVTyCltYXd2LTPiXEt172yUMgVXT1lKasB03jF+RHZTLsbWYq6JWc7C0LnI\nBBk20Uavue8bTsvpP6Zuus74d4+5FxHxgtfUKbV4q72I1OvRq/R4qHV4xOqp9Okg5P1NLMju4crA\n+QRdf+M5D/DWxKxgd80BdlTtYWvFDrZX7WFe8GyuiFiMn9u5a6uuvzSWo0XNfLK3jNkJAS7X5Hes\nkByfUdLa2U9VYzfJU3xQq+ScaC8jsyGLCH0Yc4NnOds8CTsT6O1ORICOvLJWevvN32oq5p6QSMSj\nT1D3wj/oOrCfgeoqgh/4ISp/6QT1TOpbe3lpYx5ldV34emi4e2Uihoihd5f20Krw8VBTXt+FKIrf\niKytM0gS1xISw6W6qYe6ll5mGfxxO0+KqQT0FRdR99KLWFpb0MTEEnz3fSj9R14P1WvuI7MhiwN1\nh6nsqgYGN8VLwhcxL3g2IbrxKUGskMu4Z9VUnnrtMK9tKuCpu+e4vEpgkDaA/5l5HwfrMll/4nPe\nK9rAtspdpyM4NvHCSnVuCg0eKj3B2kA8VPrTERoPlR4PtZ6IgECsvYMRnPNlI/zneD7bg1fwYPce\nerZuo3nASsDNt36rXsxd6cayqCUsCV/EwbrDfFm5iz01B9hXe4jUgBSujLyMUF3wN96jc1Oybkks\n//m8gP9uK+aH141/IaCRIK1uo+R4aQswmOZmE228X7QBgBvi17h0KFpi5MxKCKBydylZJ5qZnxz8\nrZ8rfXwJ+8WvaXr3bTp27aTy6ScJvuc+tNNSnGCtayGKIruyanl3ezEDZhvzkoK4ZWk87prhL0VR\nQR4cLWqircuEj4fm9P+7Kdy4M+km/nL0X7ye/y4Pp/94XJ2USkg4g4yCBgDmuFADUFdCtFpp+exT\nWj/7FACfVdfgu3L1iBqR2kQbxW2l7K/LILspF7PNgoBAsm8C84Jnk+yXiOIidRvjgTB/HddeEsP7\nO07wxmYjD65NdvmNtkyQMT8knSTfRNaf2EheSyFahTtRHuGDkRmVHo+TDs3X/x78v4uJDfj76Gmy\ndp335xarjawTzah8fIj60cPU/uVPdOzcjs3UT9Cd3zvnvaaSK7kkbD4LQuZwpDGbbRU7yWzIIrMh\ni2TfBJZGXkas19fKcPOTg9iXU0fWiWaOFjWPi15d9mb8f7OczPETJx2fWD/21hyipruOuUFpTPGM\ncLJlEo4izeDPx7tLySxsOqfjAyBTKgm87U40U2JofOt1av7+F3xXr8FnxaoJKXk9FDp6BnhtUwHZ\nJS1oNQq+t2IqsxNGHgmLCtJztKiJivqubzg+ANGeUSyLupxNZdt417ie7ybd7PIPXAkJZ3EqzU2t\nlDMtZuIIs9gLc1MTdS+/SH/JCRS+vgTffR9ucfHDHqelr41D9ZkcrMukpb8NgAA3P+YFzyY9OHXE\nNSWuzJWzw09uspvYn1vPgmnnfma6Gp5qPd9NunlMr2msbKen38LcqUGoPL0I//mvqPnbn+g6sB+x\n30TQvfcjU57buZLL5KQHpTI7cDDde0vFDnJbCsltKSTaM4qrIi8jyTcBQRC47SoDj/0ng/9+WcTU\nKO9JF+GdXJ/WzpgtVvIrWgn2dcdda+Oz41vQyNWsjlnubNMkHEiwr5Ywfy25Za30mSwXXDQ8Fy5C\nHRZO7QvP0fLJx/SXlRJ0973I3S9cvD/ROFY8KFPd1WtmapQ331sx9Zwy1cPhVJ1PWX0XM88hvStJ\nXEtIDI3y+i6a2vuZOzUQtVISBDmTzkMHaHzrDWx9fejT5xBw6+3DWr/NVjPZzXkcqD2Mse0EIiIq\nuYq5wWnMC55NjGfUhD6UkckE7l6RyGOvZPD2tiIMEV6TtrbkYhwpGmxaeioKI9dqCfvJz6l5/u90\nHztC7fN/I+SBH16wblgQBJL9Ekn2S+REexnbTjpALxx/lRBtEEsjL2VWwHSunhvJxv3lfLK3jBsv\njxuTz+cqSHLWI0SrVXM4v559OfUsnBZMkfUAJzrKWBWzjKm+wz8Jkrg4riR72tVrJr+ijTB/LWEn\nezedD4WXFx5z52OqqqQ3N4fuzEzcDQkoPMfH6d5o5r1/wMJbW4v4YEcJNhusWxLLLVeeX6Z6OLip\nFWw+VIlKKWNe0rfz4MezxLUr3euTick671sPV1JS08naS6IJ9h3bQxlXnXNrXx8Nr79C66cbQK4g\n6I7v4nvNWmSqodWpWG1WNpd/xcu5b3G44RjN/a1Ee0Zx9ZSl3Jb4HWYFTsdH4+00p2cs591do8RD\nqyKzsImqxm7mJQdNaGfvfFxozm02kdc2F6JUyLh5aRyyk/MjKJToZ6ef3j/0FhnRpaadN/JzJj4a\nb2YHzWSGfzL9FhPF7aVkNeWQUX+UhHAf6mrk5Ja2MSPWDy/dxBNhOp+cteT4jBCtVs0nu0oGNdHn\naNlU8xmB7v7clniDVNvjIFzpAalzV7HjaA0ikD6EnHiZWo1+zjywWunJPkbngX0o/fxQh4U73thR\nMtJ5L6nt4M/vZZNf3kZ4gI6frJvOzDh/uz3w1Eo5+3LqaO7oZ9mciHOO6650w1vjyZHGbMo7K5kb\nlDYuvp+udK9PJibjvNtEkde+KEQQBG6/KgH5GPf3cMU57ys5Qc2f/0hfkRHNlGjCfvJz3BMSh7x2\ntfS18a+cV8moP4qbXMPisPncmnA9SyMvJVwf6hL1O2M97xEBOqoau8kta8VNrSA2dHwc/NmTC815\ncXUHXx2pZm5SIKnx30wBF+Ry9LNmY26oH3R+8nLRp6YNWTHWQ6VnRsA00oNmYhNtlHSUkdtagNy/\nCrPVQmmpyCXTwiacMyr18bEzoiiSXdKMm1rO4a4diIhcF7faJRY0CccT6qclxE9LTmkL/QMWNKqL\n/94FmQy/a69HM2UK9f95ifqXXqS/tBT/76xDUEyc+8ZqOylTvb8CURRZPieCNYuiHdLELjJIpmrD\nBAAAIABJREFUzxFjE62dJnw9Ned8TXpQKvktxkGJ6/IvWRV9ld3tkJAYr5TUdNDWZWLBtCCXbDQ5\nlog2G62fb6Rl4ycgivisWIXvqmuGtT4fa8zh7cIP6bP0kRqQwk2G6yRxFQZTsO5YlkBJzSE+2lVK\n8hQfQv0vnC0xmThiHExzO9vpOYWgUBB0z/0IGg2de3bT+N5/Cb77vmFdw8/Nl3WGtSyfcgU7q/ax\nu2Y/yvBiGqylPHegijvTluOp9hj1Z3F1JvcqNwqqG7tp7ugnLK6Tko4ypvlNJcnX4GyzJMaQNIM/\nZouN4yUtw3qfbuYsIh55HFVICO1fbaP6T/+Hpb3dQVaOLQ2tvfzuraN8uq8cb72KX9w8k+9cFuuw\nDdXX/Xw6L/i6dYY1+Gq82VK+nRPtZQ6xRUJiPJKRP9i0dLKruZlbWqh+9ve0fPIxCk8vwn72S/zW\nXjdkp2fAOsB/Cz/i5dw3sdgs3JJwPXcl3SI5PWfgoVVx5/JELFYbL23Mx2K9sDz0ZEEURY4WNeKm\nljM16vxtHQSZjMDb7kQdHkHXoYOYqqtGdD0PlZ7VMct4ev6vWRZ+JYJNQZHpCI/u/x3/LfyIxt7m\nkX6UcYHk+IyQw/kNILPQqjuGQpBzXewqZ5skMcakGQZPZjILG4f9XlVQMBEPP4YubTZ9xUVUPP0E\nfcXF9jZxzBBFkZ1ZNTz+agaltZ3MTQrkybvSh9WbZyREBQ+eTp3dyPRsTklcC4LAa3nv0Gvuc6hd\nEhLjAZtN5LCxEZ2bkoRIx35XXZmuwxlUPPEIfcVF6GalEfn4U7gbEob8/pruOv6Q+Rz7ag8Rqgvm\nV7N/xPyQ9AmXOmQPZsT5sSglmMrGbj7ZKx1CweDzq6XTxPRYv4s28BZkMnzXXgeiSPOG9aO6rpvC\njVVxV7DW73sMlCUht7qzr/YQTx18lldy36aqq2ZU47sqkuMzQjILGlAGl9Jr6+byiMX4u0sSoJON\nUH8tQT7uHC9pwTRgHfb7ZRoNwfc9gN931mHt6qTqj7+nfcdXiOKFOz67Gp09Azz3UQ5vbDaikMm4\n/5ok7l2V9K3mro4gMvBUxOfCjg98LXHdZmrnXeP6cTfPEhL2xljZRmfPAGkG/4tuuCYitv5+6l/9\nD3Uv/hPRaiXwju8SfP+DyHVDS8ESRZHd1Qd4NvM56nsaWBy2gJ/P+gFB2skdPbsYN14eh5+nhk0H\nKzhR3eFsc5zO0VNqbudJczsb7bQU3OLi6ck6Rl/JiVFff8nMSCIUSXRkzmOp/2pCdcEcaczm94f/\nxvNZL1PUVjKhnpdDiuEaDIY5wB+MRuOlBoMhFfgXYAKygP8xGo02g8HwY+DGk2/ZZDQanzQYDG7A\nW0AA0AXcYTQamwwGw1zgb4AF2Go0Gs9ZgOSq9PZbyK+tRJVcjpfak6uiljjbJAknIAgCaQn+fLa/\ngpzSFtJG0JNGEAR8rlqOJjKKuhf/SePbb9JfVkbAbbcjU7p2l2uArBPNvLapgM5eM4mR3nxvReK3\neuo4Ep2bEn8vDeV1nYiieNET1kGJ6yJJ4lpCAsg4Ga0eikDLRKO/rJS6l17E3NiAOiKS4HvvRxU0\n9B4zPeZe3i78kOymXLQKd+5KuoUU/yQHWjxxcFMruHvlVP7w9lFe+iyPJ+9KH1Kd7EREFEUyjU2o\nlDKSo32G9B5BEPBdex3V//c7mtd/SNjPfjmq6KJMJnDHMgNPvnaYg/vkPHnXDyjpKmFrxQ4KWoso\naC1iikcESyMvY5pf4rgQCLoQF7XeYDD8AngZOLWb+TfwkNFoXAR0ADcbDIZo4BZgPjAXuNJgMKQA\n3wdyTr72DeCRk2P8C7gZWAjMMRgMM+33kRxPXnkr8rBCEGysjV2BWu76G1QJx3A63c04/HS3M3FP\nSCTi0SdQR02hc/9eqv7wO8wtw6sdGktMA1be2FzI3z88Tq/Jwo1LYvnpjTPG1Ok5RVSQBz39Fpo7\n+i/6WrlMzh1Tb0Ij1/B+0YYJn8ssIXE+LFYbmYWNeGpVxId7OducMUO02Wjd9BmVv38Gc2MD3suu\nJuLhR4fl9BS3lfLbjL+Q3ZRLnFc0D8/5seT0DJP4cC+WzYmgqb2f97aPPmoxXqlt7qGhtZdp0b7D\n6qHlHm/APTmFPmMhvfl5o7YjIlDP0rRwGtv72HSwkqm+Bh5KvZ+fznqQFL8kyjor+XfO6zxz6M8c\nrMvEaht+lourMBS3rQS49ox/hxmNxv0n/76PQeelClhmNBqtRqNRBJRA/8mfbT752i+AKwwGgweg\nNhqNJSdfuwW4YvQfZezYW5qN3LuRMPdwZgVMd7Y5Ek4kPEBHgLcb2SdaGDCPbiFQ+vgS/stf47Fg\nEabyMiqffoLewgI7WWo/Sms7eeLVDHZm1RLmr+WxO2ZzZXrE6b4DY01U8NDT3QD83HxYZ1iDyTrA\n6/nvjusFXEJipBRUtNHTb2F2QgCyMZawdhbm1laq//wszes/RK7TE/qTn+N//Q1DFjCw2qx8XrqV\nvx17kc6BLlZOuYofzbwXL/Xkk2a2B2sWRRPmr2NXVi3ZJybnIdQpNbdZ52jCfTH8rr0OgOb1H9ol\nFW3Noil469VsOlhBXUsPANGekdyXcgePzPkpc4Jm0djXzJsF7/P4gT+wo2ovA1bXkqIfChf9thuN\nxo8MBkPUGf9VajAYFhuNxl3AKkBrNBrNQLPBYBCAZ4FjRqOx6KSTcyqBswvwBDyAMyWYuoDoi9nh\n7e2OQuH8jtKiKFLCQRDhBwtuJcBn4kv/uRL+/npnm/AtLpkZxofbi6ls6WXetJBRjxfw8/+hfloC\nZS+/SvWfnyXqztsJWb3SqYWy/v56rFYb739VzLvbjIiiyJrFMdy2PBGVkzu9TzcE8sGOEho7+od8\nf6zwX0xJTyl7KzLY2bibG6etdrCVw8cV7/XJwGSZ9+wvB8VUrpw3xemfeSyu33LgIFXPv4Cluxuf\nObOJ/cEDKD2G/vxu7m3lhYOvUtB0Aj93H3409y4S/GMcaLHjcfbvHeAXt6fxk7/u5vUtRp6fFoLn\nBGykeSZnz3l2aQsKuYzL50YNvy7WP5nehQto3rsP2Yk8/ObPG7V9379uOr99LYN3t5fwzPfnn953\n+PvrSYmKpamnhc+MX/FV6V4+LP6ULZXbWR53GctiF6NTj23z45EykqTK7wJ/MxgMjwF7GKz1wWAw\naIBXGHRkHjj52k7g1G9ZD7Sf9X9n/v8FaWvrHYGp9kcURTQyd6K1yeit3jQ1De2UWWL0+PvrXXK+\np0YMnvZtz6gkNsg+DxJF2gLCPAOo/dfzlL/yGi25hQTe8d0hNyyzJ/7+evKKGnhpYz4ltZ1469Xc\nvXIqiZHedLQ7/3vppRl0vPJLW4Z1f6yJXEFBQzEf528mUhNFrNcUR5k4bFz1Xp/oTJZ5N1tsHMip\nxddDjY9W4dTP7Og5t5lMNL33Dh27dyKoVATcejueiy+j3STAEK+b1ZTL2wUf0GvpY4b/NG5JuA53\n3Mf1veIq97pOKWPtoil8sLOE5987xr2rJ27K4Nlz3tjWS1ltJykxvvR09dPTdfF07bPRLV9F8/4D\nlL3+NrboRAT56A4iYwK1zIj1I+tEM5/sKGbBtLNTQFWsDF/O4sBF7Krex67q/byfu5ENBVtYHb2M\ny8IXjur69uR8jv1IKpRWALcYjcbLAV9g28lIzydAttFovM9oNJ7KHdkHXH3y78uBPUajsRMYMBgM\nMSffdxWDDtS4QBAE/rjspzy+6nZnmyLhIkQG6vHz1JB1ohmzxX5pU25xcUQ++gSamFi6Mg5S+bvf\nMNA0ulqi4SKKIlsOVvD4K4cpqe1kztRAnvpeOokuJH3rrlES6O1GRX3XsML9bgo37ph6E4AkcS0x\nqcgtbaHPZGV2QqDTUlTHgv7KCiqefpyO3TtRh4cT8cjjeF26ZMjR8wGrmXeNH/NSzhuYbRZuNlzH\n3cm34q50d7Dlk4ur0iOIDNRzKL+BmqZuZ5szZhwpGnma2ylUgUF4LlzEQH0dnQf2X/wNF0EQBG5e\nGodKKeO97Sfo7jOf83V6lY6V0Vfx9Pxfc23sStwVbuS3Gkd9/bFgJI5PMfCVwWDYD3QajcZNwBpg\nMbDcYDDsPPlnHvACkGQwGPYC9wKn1NvuB94GMhhMizs02g8iIeEsBtXdAugfsJJb1mrXsRVe3oT/\n/Fd4Lr6MgeoqKp9+kp68XLte43yYLVb+8XEuz3+QhUwmcO+qqdy3OgntGMhUD5fIID29JgtN7cNz\nXmK8olguSVxLTDJOq7lNHb4S5XhAtNlo3fIFlc88hbm+Hq+lVxH+8GOoQ0KHPEZtdz3PZj7HnpoD\nhGiD+OXsH7EgdI7Um8cByGQCqxdGIQKfHahwtjljxhFjEzJBYEac36jG8Vl5DYJCQcunG7CZz+2o\nDAc/TzeuWTiF7j4zH+68sPCERqHh8ohLeGbB//JAyl2jvvZYMKRUN6PRWM6gWhtGo3EjsPGsn3/M\n16pvZ/Odc4x38NR4EhITgTRDAJsPVZJZ2MTMuJGf3pwLQaEg8LY70ERF0fj2m9T89U/4rb0O7+Ur\nHPoQfntbEUeLmkiK9uXOqwz4eo69YttQiQryIKOgkfL6LgK8h3cauyzqcgrbiiWJa4lJgWnAyrHi\nJgK83E73wZpIWNrbqX/lJXrz85B7eBB0191ok1OG/H5RFNlXe4gPizditpm5JHQea2NXopK73oHP\nRGJGrB8RAToy8htYvSCKYN/xUS8yUlo7+ymt7SQx0hu9++iUgZU+PngtuYK2rZvp2LUD7yuuHLV9\nS9PCOZBbz+7sOuYnBw9J+XG8HAqMbzFuCQkXYUqwHl8P9cl0N5tDruG5aDFhv3gYhZcXzes/pO5f\n/8DW75j0rN3ZtezOriMiUMeT985zaacHBucfoLxu+DnrZ0tcN/W6roy4hMRoyS5pZsBsI31qwLjZ\nqAyV7qxjVDzxKL35eWinpRD5xG+G5fT0mnt5Ofct3jGuRylTcO+021lnWCs5PWOAIAisWjBlMOqz\nv9zZ5jicU01LU0eR5nYmPstXINNoaP18o132BQq5jNuXJQDw5hYjFqtj9jXOQHJ8JCTsgCAIzDIE\n0GeykF9u33S3M3GLjibikSdwizfQfSSTyt8+zUBDvV2vUVbXyVtbi9BqFDy4dtqwegs4i4jAU5LW\nnRd55bk5U+L6tfx3JIlriQnL4YKTaW4JE6dpqW1ggIa336D2+b9h6+/D/6ZbCPnRj1EMQ7WtpL2c\n32b8laymHGI8p/Bw+o+Z7p/sQKslzmZmvB9h/loO5jdQ3+p84RxHYm/HR67X433lMqxdXbRt22qX\nMWNDPVk8I4Sa5h62Hq6yy5iugOT4SEjYibQE+zQzvRgKT0/CfvJzvK5YykBtLZW/eZLu7Cy7jN3V\nO8A/P87BarVx7+ok/L3c7DKuo3FTKwjycaeioQvbCOt00oNSmR04k/LOSjaVf2lnCyUknE+fyUJ2\nSQshflpC/SdGKpGptobKZ56iY8d2VCGhRDzyON6XLx1yNMsm2vii7Ev+cvQF2k0dXD1lKQ+l3oe3\nZvI0dXUVZKeiPiJ8PoGjPp29Axir2okJ9cBbbz+lVu8rr0Ku09O2dTPWbvuIRFx/aQx6dyWf7i0b\ndg2tqyI5PhISdiI6ZHARO1bU7PCwsKBQEHDjLQR9715Ei4Xa5/5Ky6cbEG0jv67NJvLvT/No6TRx\nzaIpTIv2taPFjicqWE+fyUpj28gX53WGNfhqvNlSvp0T7WV2tE5CwvlkFQ+uTekJ4z/NTRRF2nft\npPI3TzJQU43nZUuIeORx1GHhQx6j3dTB34/9m8/KtuKl9uSh1PtZMWUpMkHaGjmLWQZ/Qvy0HMhr\noNFF2pjYm6ziZkQRZsXbV1xEpnHD5+qV2Pr6aP3ic7uMqdUouXFJHAMWG29vK5oQAkDSt1tCwk7I\nBIFZBn96TRYKKtrG5Joe8+YT/qv/ReHrS8unG6j9x9+x9o7sYfHxnlLyytuYHuPLyvlR9jV0DIgK\nGkxrKa8bWbobSBLXEhObQwUNAMxOHN9qbtbeHupe/CeNb76GoFAS/MAPCbzldmSqoReJH2/K47eH\n/kJxeynT/ZP5dfpDLtXLa7IiEwRWzY/CJooTVuHtVFbILIN9hZAAPC+7DIWPD+3bv8TcZp99yNyk\nQBIjvTle0sIRY5NdxnQmkuMjIWFH0gwn090Kx67fjiYyishHnsA9cSo92VlUPvMUptqaYY1xrKiJ\nzw9UEODlxj2rpo7L3h5RQafqfEbXlE+SuJaYiHT3mckrayUiQDeuFbP6ThRT8eRjdGcexi0unsjH\nn0KfOmvI7zdbzbxftIEXc15nwDbAjYa13JN8G1qpN4/LMDshgGBfdw7k1k+Y9KpT9PabKShvIyJQ\n55BUcplShe+qaxDNZlo/+8QuYwqCwK1XxqOQC/z3yyL6TBa7jOssJMdHQsKOxIZ54qlTcbSoaUxV\nUOR6PaEP/RTvq5Zjbqin8pmn6TqSOaT31rf28vLn+agUMh5Ym4y7C/bpGQoRgToERu/4wKDEdbRn\nJEcas8moPzp64yQknMzRoiasNpH0qeNT1EC02Wj57FOq/u93WFpb8Vl1DWE/+yVK36Gn5Nb3NPDs\nkefZVb2fIG0gv0j7EYtC5437tL+JhkwmsHJ+FFabyOcTLOqTfaIFq00cVdPSi+ExfyHKoCA69u5h\noKHBLmMG+2q5em4k7d0DfLyn1C5jOgvJ8ZGQsCMyQSAtPoCefgvGqvYxvbYgl+P/nXUE3/t9EG3U\nvfA8zes/vGDdj2nAyj/W59BnsnLHsoTT6mjjEY1KQbCfdlQCB6f4WuJazXtFH0sS1xLjnsOn0twS\nxl+am6W9jeo/P0vLhvWD4i4/+yV+16xFkA9NcVIURfbXZvCHw3+npruOhSFz+GXaDwnRBTnYcomR\nkp4YQKC3G/ty6mjumDhRnyMn1dxmGRz3PRTkcvzWXAtWKy2ffGy3cVfMiyTQ242vjlRTYYcDRmch\nOT4SEnYmLWHwJGcs093ORJ8+h4iHH0XpH0Drps+o+dufz6nwIooir35RQE1zD5enhjEvefxvAiID\n9ZgGrDTYQQp1UOJ6rSRxLTHu6ewZIL+ijegQj3Gj1HiK7uwsyp94lL7CArQzZhL5+NO4GxKG/P5e\ncx+v5v2Xtws/RC5TcHfybdyUcB0q+eiaRko4FrlMdjrq88XBSmebYxf6TRZyS1sI9nUnxM+x6aa6\n1DTUEZF0ZRzEVGWf+VMq5Nx6lQFRhNc3F2Kzjc80cMnxkZCwM3FhXnhoVSdTS5zT9EsdFk7EI4/j\nnpxCb14ulc88ianqmzr8X2ZWk1HQSGyoJ+suj3WKnfYmahSNTM9FelAqaYEzJIlriXHNEWMjogjp\n4yjaYzObaXznbWqf+ytifz8BN99KyIM/Qq7TDXmM0o4Kfn/4rxxpzCbaM4qH0x9iZsA0B1otYU/m\nJgUS4OXGnuO1tHb2O9ucUXPE2MiAxeYQUYOzEWQy/K69DoDmjz+y27hJUT7MTQqkvL6LHceGV0vs\nKkiOj4SEnZHJBGbF+9PVa6aoqsNpdsi1WkJ/9BA+K1dhbmqi8ndP05lxEICiqnbe33ECD62K769J\nRiGfGEvBlJPKbmUjbGR6Lm40rJUkriXGNYcKGhGA2Ynjo75noL6Oqt8+TftX21AFBRPxv4/hteSK\nYfXm2VK+nb8cfYHW/naWR13BQzPvw0fj7WDLJeyJXCZjxfxILNaJEfXZf7wWsL+M9flwT5qGW7yB\nnuPZ9BUX223cdUvicFcr+GhXCW1dJruNO1ZMjN2OhISLkWZwbrrbKQSZDL811xHy4A8RZDLq//0v\nqt5+m399fBxRhO9fk2TXBmrOJjxQhyBg1/xjSeJaYjzT1mWiuKqduHAvl/+ui6JIx749VDz9BKaq\nSjwWXULEo0+gDo8Y8hjtpg6ey3qZT0s346HS8z8z72Vl9JXIZUOrB5JwLeYlBeHnqWFXdu243GSf\nwmyxcTi/AT9PDRGBQ49ajgZBEPC79noAmj/+0G4KpZ5aFddfGkP/gJV3v7KfQzVWSI6PhIQDiI/w\nQuem5EhRk0vkwepmziL84cdQBgbRt2Mby4o3cePcIAwRE+sEVK2UE3JK4MCO8y5JXEuMVw4XNiIy\nWCzuylj7+qh/6UUaXv0PgkxG8L3fJ+iOu5Cph+6s5TYX8LuMv1LUdoIUvyR+nf4Qcd4xDrRawtEo\n5DJWzIvEYrXxxaHxq/CWX95Kn8lCarz/mKoIusXGoU2ZTl+Rkd68HLuNe8mMEGJCPDhc2EhO6fgS\n/5EcHwkJByCXyUiN96ezZ4Di6rFVdzsf6pAQDi24hSJtOFF99UR//jL95eXONsvuRAXpGTDbqGvp\nseu4y6IuZ4qHJHE9EbHZRI4VNdHTZ3a2KXbncEEDgvB1jzFXpK+0lMqnHqMr4yCa6BgiH3sKffqc\nIb/fJtr4qHgjLxx/lX6riRvi13DvtNvRKcdvvyKJr1kwLRhfDzW7smrp6B6fUZ9Tam7O+B76rb0e\nBIHm9R9dUOV1OMgEgduXJSATBN7aamTAPH7EfyTHR0LCQZySjc0sdI1Oxwfz6tl6vImDySvwXHkN\nlrZWqn7/Gzr27XW2aXYl6mSdjz36+ZyJXCbnziRJ4noismFvKc+tz+E3rx5ymiCJI2hu76OktpPE\nSG88tK6nYibabFSv30DVH57B3NyMz9UrCf/Fr1H6D6/4e0v5DrZX7SHQPYCfz/oBi8PmS715JhAK\nuYyr50Vhttj44tD4q/Wx2mxkFTfj46EmOtRjzK+vDg9Hnz4HU2UF3UPs7zcUwgN0XDk7nKb2fj47\nUG63cR2N5PhISDgIQ4QXWo2CzKLGUfeVGS3Vjd28trkQjUrOg9dOI3DNWkJ++D8ISiUNr75M43/f\nRLSM727Mp4gKOqns5oA+A5LE9cQjr7yVz/cPptDklrSwYc/EEbA4fLLGMN0FRQ0sHe3U/O3PVLz+\nJnKdnrCf/By/a69HUCiGNU5BSxGfl23FW+3FT1K/T5g+xEEWSziThdOC8dar2Xmshs6eAWebMyyK\nKtvp7jMzNzkYmZMcct/Va0Eup3nDekSr/Z5bqxdG4eOh5ouDldQ22zfLwlFIjo+EhINQyGXMjPen\no3uAkhrnqbv19pt5/uMcBsw2vrdiKsG+g+kfupQZRDzyOKqQUNq3f0X1n/4PS4drpOWNhvAAHTJB\noNyOym5nIklcTxw6egZ4eWM+MpnAT2+cQZCvO58fqOB4ycSI5h0qaEAuE0h1YJf4kdCTm0PFE4/R\nm5eL96xUIp94CvfEqcMep6WvjVfz/4tckHHPtNvQqaTUtomKUiHj6rmRDFhsbMkYX1GfzJNpbvOn\nOc8pVwUG4rlwEeaGejrtmOWhUSm4ZWk8VpvI+ztO2G1cRyI5PhISDsTZ6W42UeTlzwpobOvj6rmR\n3+ofoAoMIuLhR9HNSqOvuIiKp5+gr2R8LF7nQ6WUE+qvpaqh22FpS5LE9fjHJor857N8OnoGuP7S\nGJKifPjl7bNRyGW8tDFv3PcNqW/tpbKhm6QpPujclM42BwDRYqHpg3ep+eufsPb24L/uJhIffRiF\nfvjpP2abhf/kvkWPuZfr468h0iPcARZLuBKXTA/GS6di+9EaunrHR9THJoocLWpCq1GQHOPrVFt8\nVl6DoFTSsvETbGb7zd/MOH+uSg8nzH9s1OpGi+T4SEg4kMRIb9zVCjKNzkl3+/xABVknmkmM9Gbt\nJVPO+RqZRkPw/Q/id90NWDs6qH7297Tv3jm2htqZyCA9AxYbdc29Dhlfkrge/2zJqCS3rJWUGF+W\nzh7cNMeGeXHzFXH09Ft4YUMuFuv4rffJKGgAXEfNbaCxkcrfP0Pbls0oAwOJePhRvJdeNeJanA+L\nPqGiq4o5QbNYGDJ0IQSJ8YtSIWf53EhMZitbD1dd/A0uQGlNJx3dA8yM80fu5H55Sm9vvJZcgaWt\nlY4dO+w69rolcVx/6fhQUJQcHwkJB6KQy5gZ50dbl4myWsekXp2P3LIWNuwuxcdDzX3XJCGXnf/r\nLggCPsuvJvShnyKo1TS+8RoNb7yGzTw+Va6mnKzzsWcj07OJ8YpimSRxPS4pqelg/a5SPHUq7lqR\n+I28+8UzQpg7NZCS2k4+3FniRCtHR0ZB48n1x/lpbp0H91P51GOYysvwmL+AyEefQBMZNeLxDtZl\nsrf2EKG6YG40rJWEDCYRi6eH4KFV8eWRarrHgQrjkaLBOruzsy2chc/yFcjc3Gjd9Bm2/sl5YCc5\nPhISDibtVLqbceyamTa39/HiJ3nI5QIPrJmGh/vQFJ20SclEPjLYMLBj906qn/09pubxV+8QFewY\nZbezWS5JXI87evvNvPhpHjabyL2rkr713RAEgduXGQj2dWfr4SqOGF1DlXE4VDd1U9vcQ0qML27q\n4YkF2BNbfz/1r7xE/cv/RhQh6O57CbrrHmQatxGPWdVVy7vG9bgpNNyTfDsqueup1Uk4DpVSzvI5\nEZgGXD/qI4oiR4xNaFRypkb5ONscAOQ6Hd5XLcfa3UXb1i3ONscpSI6PhISDmRrlg5taTmZh05hE\nBcwWK//4OJeefgs3L40nOmR4+fNKf3/Cf/W/6OfMo7+0hKz/+QldGYccZK1jCPPXIZcJVDjY8Tlb\n4rq5b/w5iZMJURR5bbOR5o5+Vi2IIjHy3A18NSoFD6xJRqWQ8cqmfBrbHJMy6ShcIc2tv7KCiqef\noHP/PtRRU4h87Ek85s4f1Zi95l5eznkDs83CHVNvxN/duTUTEs7h0pmheLgr+epIFT39rhv1qWzo\nprmjn+mxfigVrrPd9r7iSuR6D9q2bsba5dhnpCviOr8JCYkJilIhY0asHy2d/Q6PQIiiyJtbi6ho\n6GJhSjCLp49MRUamVhN0970E3Ho7NrOZun+/QN1LL2LtHR9ylUqFjFB/LZUN3Q6v0/Bs2PGWAAAg\nAElEQVRz8+GG+DWYrAO8VfABNnH81oVMdHZl15JZ2Eh8mCerFkRd8LWh/jpuu8pAn8nKPzfkYraM\nD+lyURTJKGhEpZQxPcbPKddv27aFqt8+jbmhHu+rlhHxq/9FFTg6SW2baOP1/Pdo7m/lqsglTPMb\nvgqcxMRArZRz1ZwI+kxWtrlw1Od0mpuLqSrKNBp8VqzC1t9P66bPnG3OmCM5PhISY8DpdLdCx6a7\n7c6uZe/xOiID9dy6NH5Uue+CIOB16RJm/OWPaKZE03XoABVPPEpvYYEdLXYcUUEeWKy2MektkB6U\nSopfEsXtpeypOejw60kMn+qmbt75shitRsG9qy9c83aKBdOCuWR6MJUN3bzz1fhQO6xo6KKxrY8Z\nsX6oVfIxvbalq5Pa5/5K03vvIHNzJ/Shn+D/nRuH3ZvnXGyt2EluSwEJ3nGsjL7SDtZKjGcumxmK\nzk3JtsxqevtdswfdEWMTSoWMadGuF5n0XHwpCh9f2nd8hbl1cmUqSI6PhMQYkDzFB7VKzuHCRoel\nu5XWdvL2tiK0GgUPrk1GpbTPpsctNITwXz6M7+o1WNrbqf7jH2h67x27ymE6gqhgxzUyPRtBELjR\ncC3uCjc2lGySUt5cDJPZyr8+ycNssXHXikR8PDRDfu/NV8QT5q9j57EaDubVO9BK+5BR4Jympb0F\n+VQ88Rg9x7Nxn5pE5BNPoU1OscvYha3FfFa6BW+1F3cm3YRMkLYukx2NSsFV6eH0mSx8dcT1oj61\nzT3UtfSefva7GjKlEt/VaxAtFlo2fuJsc8YUafWQ+H/27js8qvtK+Pj3TtFoNBr1jpBGILgSiGZ6\nc8UFY4NxS+KWOO5Odt+8m7Jx4u4k3mQ32eRNc+LYjpPYcRxjjLExuAVTbIzpCNAFdVDvvUx7/5AE\nGFOENJo7Mzqf5/GDGF3dOXORZ+bM75zzE35gNhmZnp1AfUs35TXtPj9/a2cvv3tjP263l/tWTCYh\nZujNw6ejmEzEL7+Osd9/GHNyMk3vbaD8R0/SczRwN5JzpPgv8QGItti5aeIKeqXkLeD8/f3DVNZ3\nsGRm+nlPOQszG/nGyjzCw4y8uF4L6N3JvV4vnx2qwWoxMmWcf5qpvS4X9a+/xrFf/Dfu9jYSbryZ\nMd/6NqboGJ+cv6m7mRcOvIxBMXBX3m3Yw4JjrxAx8i69IB1buIl3PztKV09grfrs7N+0dJYaGOPk\nTydq/gLCUlJp3bqF3urA/1DHVyTxEcJPBp4AfT3dzePx8oc1B2hs7eG6C8eRlzVyy+rWcePIfPRJ\noi++lN6KY5T/+Eka16/DO0IbhQ7HmIRITEaF0ir/jRGfnTyDKQmTONJczBYpeQsInx6sYdPeKjKS\nI7npkuwhnSM5LoI7r86lx+nm92/k09MbmP0+RZWtNLT2MGNCImbTyH/K7Kyv4+h//xeN697CnJDA\n2P/8IXFXXY0yiDLCQZ3f4+LZ/L/S7uzgxgnLyYrO8Ml5RWiwWkxcMSeDjm4XH+46pnc4n7NTq8Vo\nUJiWHXhlbgMUo5H4ldeDx0PDmtf1DsdvJPERwk+mjIvDYvZ9udvrm4o5VNbE9OwEls3P9Nl5z8Rg\nsZB82x2k/fv/xRARQf1rr3Lsf36Ksz6wxv6aTQbSEyM5VjfyAw4GKIrCV/pL3lYXraO+q9Ev9ytO\nr7a5ixfXF2AJM/LAirxhTVaanZPEZTPTqajv4K/vagG5b9P2gwPT3Ea+zK1tx3bKnniU7qJC7HPm\nkfHok1jHjfPpfaw6spay1qPMSbmAxWPm+fTcIjRcdkE6ERYTG7Yfpbs3MFZ96pq7KK9pJ9cRS0S4\nWe9wzirygllYHFm0fbad7vIyvcPxC0l8hPCTMLORqePjqW3q4mitb8rddmp1rNtWRlKslbuv+fxG\njCMtcuo0Mp/4EZEzZtJ1WKPs8Udo/XhrQL0hdKTYcbm9VNT5rzwp2hJ1vOTtJSl5043L7eEPa/Lp\n7nVzxxUqyXERwz7nzZdkk5Vq5+P8ajbvq/JBlL7j8Xj5TKvFFm5ikuP0Y7p9cj89PdT85QWqnvkd\nXreb5DvvIuWe+zBafVte+2nVTjZXfMKYyFS+ol4vm5SK04oIN3HF7LG0dzn5164KvcMBOL73V6BN\nczsdRVFIWHkDAPWvr9I5Gv+QxEcIPzqxmenwV0eqGjp47u2DhJkMfHPlFF0+WTLZo0h98Jsk33kX\nANXPP0vVM7/F3e77PqahGNjItKTaf+VuMFDylsvh5iK2VATXHkihYtVHRZRUtbEwL4X5eSk+OafZ\nZOCBFXnYwk289N5hymsCZw+Mw0ebaWnvZaaahMk4Mi/tHfn7KXvsYVo2fYRlbAaZjz5B9MLFPk9K\nKtqr+Hv/JqV3590um5SKs1oyKx2rxcj67eUBUYa663AdisJ59xPqJWLSZKw5uXTm76PzsKZ3OCNO\nEh8h/GjquHjCTAZ2DLPcrbvXxW9X932a/bWlOaQn6dfwqygK0QsXk/nYU1gnTKR95w5KH3uYjvx9\nusU04PiAgyr/vkEdmPJmNVlZXfQ2DVLy5lf7iurZsP0oKXER3HrFRJ+eOyHGyl3XTMLp8vD7N/ID\npql6e/+o/LkjsGmpq7WVqmf/QMUvf46zsYHYq65m7A8eISwl1ef31ens4o/7/4LT4+SO3C+RFOH/\nvYhEcIkIN7Nk5ljaOp38a7e+qz5NbT0UVrQwMT2GKFtwJOyfX/V5LaCqNkaCJD5C+JElzMiU8fFU\nN3ZSMcTpUF6vlxfWFRyfUjVvsm8+zR4uc2Ii6d/9Pgk33IS7vY2KX/6Cmpf+iqenR7eY0hJsmIwG\nyvw02e1kMZZobpqwvG/KW0Hov5gEiqa2Hv701iFMRgP3r5hMeNjw95A51fTsBJbOzaCmqYsX3inQ\n/d/W5fawo6CWKFsYaobvyty8Xi8tWzdT+shDtH36CRZHFpmPPE7ijTdjMPt+hdnj9fCXQ/+gvquB\nKzIvYWriZJ/fhwhNl88eS3hY/6qPU79Vn91H+svc1OBY7RlgHZ+NbfoMuguP0LF/r97hjChJfITw\ns+PT3Ya4mel7nx3ls4JastOjufnSoU2pGimKwUDc0mVk/PBRwtLSaPnXB5Q9+RjdJcW6xGMyGhib\n1DfgwOny/4vhnJQLyIvP5XBTIVsqpeRtpHk8Xp5de4D2LidfujSbjGT7iN3X9ReNY2J6NDsKavlQ\n596CgrIm2ruczFaTMBh8U3bWW1PDsZ//jJoXnsPrcpH45VvJ+MEjWMaO3GS198o2sr/+IBNjs7km\nSzYpFYMXaTVz2cx0Wjt62bSnUrc4Bvp7LgiC/p5TJay8ARSFhtWrAnJSq69I4iOEn00dH4/ZZDj+\nBHk+tPImXv1XEdG2MB68Lm/EavmHKzwjk4xHHidmyRU4a6op/68f07B2DV63/5MPR6odt8fLMT8O\nOBigKApfyekveSt8S0reRthbn5RSUN7MjAkJXHrBmBG9L6PBwH0r8rBHmHnlgyOU+HFs+qkGNi2d\n7YMyN6/LReO6tyh7/GG6Cg5hmzoNx5M/IXbJ5T4bU306BY1HWFu8gRhLNF+ffAtGQ+Bt+igC2xWz\nx2IxG1n3aZkuH3S1dfailTczLi3qvDZJDhSWMenY586j5+hR2nZs1zucEROY75qECGFWi4m8rDgq\n6jvOazPEprYefr/mAIoCD1yXR0ykZQSjHD6DOYykL99C+re/hykqioY1qzn605/QW1Pj1zhO9Pno\n88Y0xhLNjROupcfdy0tS8jZiDh9tZs2WEuKiLNx5da5fpoDF2i3cu3wyHo+X363Op73LOeL3eSqn\ny8POw3XE2i1kp0cP61xdxUWUPfU49a+/hsFqJfX+B0n7t29hjh/ZvUhO3qT0btmkVAyRPSKMS2eO\noaW9l017/T91cc+Rejxeb1BMczuT+BUrwWik4Y3VeF2B0b/oa5L4CKGDE9PdBlfu5nL3NVK3dvRy\n8yXZTBzrm13R/SEidxKZj/8I+5x5dBcXUfbkozRv2ui3BMCR0jfZrVSHPp8Bc1Nmkhefg9ZUyFYp\nefO59i4nf3jzAAoK9y2fTKTVfxMOJzviWL4oi4bWbp576yAePye2B0oa6epxMTsnacjj7D3dXdS+\n/DeOPv0jeiuOEX3hxTieehr7rDkjnkC63C6ey/8b7c4ObphwLVnRI78XmQhdV87JIMxsYN22Mpwu\n/5Zr7TzcX+YWZP09JwtLTCL6wotw1tbQsnWL3uGMCEl8hNDB9OwETEaFHQWDK3f7xweFFFa0MCc3\niSWz0kc4Ot8z2myk3ns/Kffcj2I0UvuXP1P561/iamkZ8ftOS4jAbDLomvj0lbzdgNUUzuuFb9HQ\n1aRbLKHG6/Xy/NuHaGrr4brFWUxI9/+HAtcucDDJEcveogY2bC/3631vP9S3gjp30tA2LW3fs5vS\nR35I84fvE5acQvr3HiL5jq9htNl8GeYZvbjnNUpay5mdPIMLx8z3y32K0BUVEcalM9Jpauthyz7/\n9fp09bg4WNpIemIkybHD3zNMT/HLlqOEhdGw9g08vb16h+NzkvgIoYO+crd4jtW1U93YedZjP8mv\n5oNdxxiTYONrS3OCeiO/qLnzyHz8Kaw5uXTs20vZ4w/Tvmf3iN6n0WAgIzmSiroOenWc9hNjieaG\nCcvpcffyspS8+cwHO4+xp7Ce3MxYrp6nz2qBwaBw77WTiYkMY9XGYg4fbfbL/fY43ewurCcxJvx4\nSedguZqbqfz9b6j8za9wtbYQd+0KMh57koiJ6ghF+0Xbq3exofAj0mwpfCXnhqB+bhOB48q5GZhN\nBt7eVobL7Z9Vn71F9bjcXmYF8WrPAFNMDDGXXY67uZnmf32gdzg+J4mPEDoZGHd5tulu5TVtvLi+\nAKvFyDeunzIio3n9zRwXT/p/fJfEL30FT1cXlb/5FdV/fh5Pd9eI3acjOQqP18vROn03Vp2XMpNJ\n8SoFTUf4uDJ0m0f9pay6jVf/VYg9wsw9107y2USzoYiyhXH/ijwAnlnTV5Y60vYXNdDT62ZObvKg\nkwavx0PzR/+i9JGHaN+5A+uEiWQ+9hQJK1aOyIjqM6lor+LlglVYzeHcM+V2LLJJqfCRaFsYF08f\nQ2NrD1v2+6fX5/g0txBIfADirroag9VK47q3cHee/cPZYCOJjxA6mTEhAaNBOWOfT0e3k9+u3k+v\ny8PdyyaREhfcy+cnUwwGYi+/koxHnsAyNoPWLZsoe+JRugqPjMj9OVL12cj0VIqicIt6ouStsVtK\n3oaqq8fFM2vycbm93H3NpIAY9jFxbAw3XDSO5vZe/rj2AB7PyK7qDZS5zc4Z3DS3nsoKjv7saWr/\n+iIASbd/jfTvfh9LWtqIxXg6Xa4unu3fpPQbc75KUkRovFkUgWPpvAxMRgNvfzzyqz49Tjf7ixtI\njotgTIJ/SkRHmtFmI/aqq/F0dND03ga9w/EpSXyE0ElEuJnJWXGU17RT2/T5T1Q8Xi9/WnuQuuZu\nls3PZEYQT4k5G8uYMWT88FFily7DWV/P0Z/+hPrVq3w+Teb4ZLdq/UYOD4gNj+GG7GvpdvfwcsEq\nKXkbor+9e5iapi6umpvBlHEjO3XsfFw5N4Np4+M5WNrE2o9LR+x+unpc7C1qIDU+grFJZ5+C5nE6\nqV+zmrInHqW78AiRM2fheOppYi66eERHVJ+O1+vlrwdfpa6rgcszLmZO+nS/3r8YHWIiLVw0PY2G\n1m4+ya8e0fvKL26k1+lh5sTEkCrXjF1yBcaoKJre3YCrTf/XTl+RxEcIHR0vdztlT5+3Pi5lb1ED\nkx2xrFw8To/Q/EYxmUi84SbSv/t9TPHxNL69lvKfPEVPpe8aU1PjbYSZDZTpOODgZPNSZzEpTuVQ\n42E+qfpM73CCztb9VXxyoJpxaVFcf2Fg/f9hUBTuumYS8VHhvLmlhAOlI7N3097CepwuD7Nzks76\nZqvzsEbZE4/QuHYNpqho0r75f0h74JuYYvSZDPle+Ub21h9gYsx4rh13pS4xiNHh6nmZmIwKb31S\nOqKrPrsO91VtzAyRMrcBBouFuGuW4+3ppvHtt/QOx2cGlfioqjpXVdWN/V9foKrqdlVVN6uq+mtV\nVQ39t9+jquoOVVW3qap6Tf9tVlVVV/Ufu05V+34rVFWdp6rqp6qqblVV9bERemxCBLwZExL7yt1O\n6vPZX9zAms0lxEf17RGiZ9+CP0VMVMl87CmiFi6mp7yM8qceo+mD93yyg7TBoJCRbKeivoMeHQcc\nDFAUhVtybiDcGM6qI2/R1O2fZvhQUNXQwd/ePYzVYuS+5ZMDchPfSKuZB67Lw2BQ+OObB2hq6/H5\nfQxsWjon9/TT3NwdHdT85QWO/expnDU1xFy6hMwnf0zk9Bk+j2WwtMZC3ixaT3RYFHfmySalYmTF\n2i0snpZGXXM3nx4cmf3jXG4PewobiI+ynPeAkWAQc+HFmBISaNn4Ic6GBr3D8YlzvmKoqvo94E/A\nwDa0fwS+pWnaYqAFuEVV1RTg34GFwJXA06qqWoAHgP39x/4FeLj/HM8AtwCLgLmqqur3TCyEjiKt\nZnIzYymtbqO+uYu65i7++OYBjEaFB1dOwR4xuhp+jVYrKXfeReoD30SxWKj7+0tU/OoXOJuG3wvj\nSLHj9cLRGn0HHAyIDY/hhgnX0u3ulo1NB8npcvPMmgP0ON18bWkuiTFWvUM6o3FpUXzp0mzaOp38\nYU0+bh8k8AM6up3sL24gPTGStFN6CrxeL22fbaf0kYdo2fQRYWPSGfvQwyTdchtGq37Xq6m7mecP\nvISiKNw95TaiwkLvTaIIPFfPzcRoUFj7calP/x8ccKisia4eFxdMPPvKa7BSTCYSlq/E63LRsPYN\nvcPxicF8VFYEXH/S39M1Tfu4/+ut9CUvc4Ctmqb1aJrWAhQCU/u/t77/2HeAJaqqRgEWTdOKNE3z\nAhuAJcN/KEIEp4HNTD85UM1vV++no9vFbVeoZKVG6RyZfuwzZ+F44kdE5E2l80A+ZY89TNuO4U1B\ny+rfyLQkAPp8BsxPnUVu3MT+krcdeocT8F79sIijte1cND1t0A39erpsZjqzcpI4fKyF1zcV++y8\nuw7X4fZ4mTvp89fA2dBA5a9/SdUffoens5OE628k85HHsY4b77P7HgqX56RNSrOvZVy0Q9d4xOgR\nHx3O4qmp1DZ1sf3g4DYMPx87tdAsczuZfd58wtLG0Lp1C71V/tsbaaScczaupmmrVFV1nHRTsaqq\nF2ma9hFwLWADouhb/RnQBkSfcvvJt7Wecuw5i7RjYyMwmQJrWTwxUT6x8rdQvOZL5jn4ywaNN7aU\n4PXCFXMzuWGJ//bSGAxdrnuinZQfPUr1+ncpff7PVD3zO1wX5zPunrsxRZ7/5JwZk4C3DlLT3B1Q\nv0f/tvCrfPudp3i9aC2LJswgPiIWCM3f9eH4ZH8VH+w6RkaKnW9+acaIjXb39XX/7u2z+Nb/fsQ7\n28qZNTmVOZNShn3OvUV9fUNXLRxHYrwNr9tN1bp3KPvb3/F0dxM9dQrjH7gXq5+ntZ3J87v+QUlr\nOQszZnHjjCu/8Mm4/K7rY7Rc99uWTWbzvirWfVrOsouyMfqofNzt8bKnsIEYu4V509MHdd5gvebG\nr95KwdM/o+2dteT853f0DmdYhvLKcSfwK1VVHwU2Az30JTIn/2vageZTbj/dbSffflZNTYE1Rzwx\n0U5dXWA0So8WoXzNczJiOFjahCPFzg2LHQH1OPW+7qZZC8hIH0fVn/5A3cZNNO3LJ+XOu4nInXRe\n5wnDiyXMSEFpY0BdXzCzMnsZLxes4tdbX+TBaV8nKSkqwGLUV0NLN796ZRdhJgP3LMulraWLkbg6\nI/W7ft+1k/jxX3fyi5d28tjXZpMwjBK91s5e9hyuIyvVjtHj4diuA1S/+AI9pSUYbDaS77ybqAUL\naVcU2gPgd2hH9W7WH9lIqi2ZG7Kuo77+86Wmej+/jFaj6bobgIVTUti0t4p1mwuZ54MPHwAKyppo\n7ejl4ulpNDacu4Q6mK+5d1wu4VnjaPj4E45+tp9wh0PvkM7pTEnmULpClwG3app2GRAPvAdsBxar\nqhquqmo0kAvk01cKd3X/zy0FNmua1gr0qqo6XlVVhb6eoM1DiEOIkLF0bia5mbE8uDIPc4CtbAaC\nsJQUMr7/Q+KXX4eruZljP/8Zx375c7pLSwd9DoNBITPZTlV9B929vh2XPVwLUueQGzeRg40a26Tk\n7XPcHg9/WHuAjm4Xt1w+kTGJZx/dHIgyku3cevlEOrpd/H5N/rAmTO3U6vB4vczJjqXutVcpe+px\nekpLsM+dj+Opp4leuChgeg0q26t5qeA1wo0W7plyh2xSKnRz9XwHBkVh7dZSPD7qp9x5uG8a60w1\n8Mtuh0tRFBKuvxGA+tWv6RzN8Awl8TkCfKCq6sdAq6Zp6zRNqwb+H30JzIfADzVN6wZ+D0xWVXUL\ncC/wRP857gdeoi9h2q1p2qfDfBxCBLXJWXF89yszSIgO3GZtvSkmE/HLryPjB49gzcmlM38/5T96\nnMrf/ZqeiopBncORYscLlAfIgIMBJ6a8WVhVuJaGTtnYdMCaLSUUHmthTm4Si6em6h3OkC2emsqC\nvBRKqtp49cPCIZ/ns0M1ODorGf/mMzStX4cpLo4x3/o2qffchykqcPoCu1zdPJv/F3o9Tm7PvZlk\n2aRU6Cgpxsr8vGSqGjrZecr2EUPh8XrZdbgOW7gJNUOf0fD+FpE7iYjcSXQeyKdTK9A7nCEbVKmb\npmmlwLz+r9cCa09zzLPAs6fc1gncdJpjtw2cTwghzke4I4ux3/lPOg8dpH71Ktp37aR99y7sc+cR\nf+11hCWffrwvnLyRaRsTxwbWi1VceCzXZ1/Dy9oq/rjjZe7KuT1gPrnXy8HSRt7+uIyE6HDuuDIn\nqK+HoijcfoVKWXUb7+88xoSxMec9oKGhqp7sz94ir60Yj8FA7JVLiV9+HQaLZYSiHhqv18vfDr1K\nbWc9SzIuYnrSFL1DEoJrFjj4OL+aN7eWMFNNxDCM55OSqlaa2npYmJcSkCP1R0r8yhvpPPQk9a+/\nxtjv/zAon5NHz7+WECKkROROYuxDD5P2b9/Ckp5O27ZPKH3kIWr+8gLOxtPvN+Don5RXGkCT3U62\nIG0OObET2F2Vz7bqnXqHo6vWjl6eXXsQg0Hh/hV5RISPzDADf7KEGXngujwsZiMvrDtETePgele9\nXi+tH2+l9sePktdWTG9iGhkPP0biTV8KuKQH4P3yj9hTl8+EmHEsH3eV3uEIAUBybATzJqVQUdfB\n7sPDW/UZWDW6IISnuZ2Oddw4ImfMpLuokI69e/QOZ0gk8RFCBC1FUYicNp2MR54g9b4HMScl0bLp\nI0p/8J/UvvISrpaWzx2fFGvFajFSWhWYDaaKonBr7o1YTeGsOvImzT0t5/6hEOTxevnTWwdp6ejl\nhovGMy4tcEq4histwcZXr1Lp7nXzuzfy6T3Hhrq9tbVU/OJ/qH7+Wby9vXyQMIv07z9MeEamnyI+\nP4ebilhT9A7RYXa+nnerbFIqAso1CzJRFHhzGL0+Xq+XXVodFrORvKw4H0cY+OJXXg+KQv3qVT7Z\nYNzfJPERQgQ9xWDAPnsOjid+TPKdd2GMiaH5/fcoeei71L/+Gu6ODgAMSt+Ag5rGTrp6AmvAwYC4\n8Fhun349Xa5u/l6walRubLphezn5JY1MGRfPFXPG6h2Oz82bnMLF09M4WtvOy+8fPu0x7o4OGtau\noeyxH9J56ADmnMk8O3Y57dMWEhMd4eeIB6e5p4Xn8/s2Kb0r73bZpFQEnNR4G3Nzkzla287eI/VD\nOsfR2nZqm7uYOj5+VA4jsqSNIWr+AnorjtG2fZve4Zw3SXyEECFDMRqJXriYrB/9F0m33o7BGkHj\nurco+f53aHjrTTzdXThSovoHHATmqg/AZeMWkRM7gfyGArZX79I7HL8qqmzh9Y+KiY4M465lucOq\nww9kX1kygYzkSDbtrWLr/qrjtzsb6ql95SWKv/cfNKxZjSHcSsq991Ow6CZazJHMyQ3MCVIDm5S2\nOdu5Pvsaxsc49A5JiNO6ZoEDhb5Vn6F8sLTr+DS30VXmdrL45deB0UjDmtV4XYH5IeKZSOIjhAg5\nislEzCWXkfWTn5Jw05f6nqDfeJ2S738PtWI3Jo+LkgAtd4OBKW83YjGG8c9RVPLW2e3kD2sO4PF4\nufeaSUTZQnf8sdlk5MHr8rBajPz1XY3yvYeo+uMzlDz0PZrffw9jhI2Em76E4yc/JWrOPLYX1GE0\nKAE7Ond14dsUt5QxM2kaF6cv1DscIc4oLcHG7Nwkymra2Ft0+n7Qs9mp1WEyGpgyLn4EogsO5oRE\nYi66GGddHS1bNukdznmRxEcIEbIMFgtxVy4l6+n/Jn7FSrxuF9aP3ubesjdwfro5oD+pirfGsjL7\nGrpcXaOi5M3r9fLn9Rr1Ld0sW+Ag1xH6tfOJMVbuyTVwfel6un/9U9q2byMsNY2Uu+4h6+mfEXfl\nUoxWKzVNnZRVtzHJEUek1ax32F+wo3o3G49tJcWWzC05NwblpCcxulyzwAHA2q0l5/XcWtXQQUV9\nB3lZcVgtwT9wZTjili1HCQujYe2beHp69A5n0CTxEUKEPKPVSvy1K8h6+r+JXXo1Vk8v6r73KH34\nIVo/3hqwDZqL0uYyMTZ7VJS8fbS3kh0FtUxIj2bFIofe4Ywor8tF6ycfU/bEo0S89hyOrmpKrSns\nnXcjGY89SdT8hSimE2+qth+qBQjIMreBTUotxjDuybudcFPgTZkT4lTpiZHMVBMpqWpjf3HjoH9O\nytxOMEVHE7vkCtwtzTR/+IHe4QyaJD5CiFHDGBlJ4g0388HCr7EjOgdncxPVzz9L2WMP07bjs4BL\ngBRF4bacGwnrL3lr6QnMMdzDdayunb+/fwRbuIn7lk/GaAjNlyZPdxdN766n5H60QHcAACAASURB\nVAffo/q5P9JbWYF9zlzG/PAxPpt9I+/UR7BpX9UXfm77oRpMRoUZEwLrzVaXq5s/5f+VXo+T23Jv\nJsUWeImZEGdybf+qz5vnseqzU+srOZ2WnTCCkQWP2KuWYoiw0fjO27g7O/QOZ1BC89VFCCHOIjUz\nhfcT5+B+8AdELb6Q3ppqqp75LeU/eoL2fXsDqqws3hrHyvHL+kretNAreetxunlmzQGcLg9fvzqX\nuKhwvUPyOVdzE3WvvUrxd/+Duldfwd3eTsxll5P1k5+Seu8D2LKyuH9FHrZwEy+/d4Sy6hP9ZxV1\n7VTUdTBlXHxA7WXUt0npP6nprOOysRdyQdJUvUMS4rxkJNuZMSGB4spWDpY2nfP4hpZuSqvbyMmI\nCciSUz0YI2zELb0aT2cHTRvW6x3OoEjiI4QYdY5vZNplJOWrX8fx1E+wz51Hz9FyKv/f/3L0v35M\nZ8EhnaM8YdGYvpK3/fWH+Kxmt97h+NTf3z9CZX0Hl81MZ8bEwFrRGK6eygqqX3iO4v/8Dk3r16GY\nzMRfdz3jfvYLkr5yK+aEE483Pjqce66djMvt4Xdv7Kezu6//7ESZW7Iuj+FMPji6iT11+8mOyWLF\n+KV6hyPEkCxfmAXAmkGs+uw8XuYmK5sni7l0CabYWDoO5OsdyqAEzsdHQgjhJ46Uvv1FBjYyDUtO\nIfWe+4lbuoz6Navp2L2LY//zUyJyJxO/8nqs48brGS4GxcCtOTfy4+2/4J+H16DGZhNtCf5NPbcf\nqmHT3koykiK5+RJ9r7GveL1eug5rNG14h459ewEwJ6cQe+VVRM1fgMF85kl1U8fHs2x+Jm9/UsYL\n6w7x4Mo8thfUEmYyMC07cCZIHWkqPrFJ6eTbZJNSEbQyU+xMz05gT2E9BWVNZx2qskurRQFmTJAy\nt5MZLBbG/uBRCJJqBEl8hBCjTkJ0OLZwE6XVn++ZsaSPZcw3/p3ukmLq33idzgP5dB46gG3adBKu\nuwHLWP0200ywxrFy/NX84/Ab/F17nfumfDWop2fVNnfx4voCLGYj91+XF/QbAXo9Htp37aBx/Tv0\nlJYAED4+m7irlmKbNgNlkH1L1y3OovBYCzsP1/HCOwXUNHYyOyeJ8LDAeLlu7mnhuQN/A+DrebcR\nbZFNSkVwu3ahgz2F9by5tfSMiU9Lew9HjrUwIT2a6EgZ4HEqc2ys3iEMWmA8kwohhB8pioIjxc6B\n0iY6up3Ywj9frx2eNY70//sdOg9rNKxeRcfePXTs3YN99hziV6wkLCVVl7gXjZnHrtp97K8/yI6a\nPcxOmaFLHMPlcnv4w5p8unrc3H1NLilxEXqHNGSenh5aP95C07sbcNbVgqJgm3EBcVcuxZo94bzP\nZzQYuG/FZB5/4TO29A86CJRpbl2uLn6393naetu5IfsasmOy9A5JiGHLSo1i6vh49hU1oJU3oWZ8\n8U387iP1eIELpMwt6EmPjxBiVDre51N95o1MIyaqpH/vIcZ86z+wZDpo+2w7pY/8gOoXnsPZUO+v\nUI8zKAZuy725b8rb4TW09ATuJqxn8/pHxZRUtbEgL4UFefokkcPlamulfs1qSv7zO9S+9FdcTY1E\nX3gxjqeeZsw3/n1ISc+AmEgL9y2fjKJAeJgxIDZK7HU7eWbfn6lor2JR2lwuGbtY75CE8JlrFzoA\neHNr6Wm/v1Pr67WbGWJ9iKORrPgIIUalE30+rUw+S123oijY8qYSMXkK7bt20rDmdVq3bqZ128dE\nX3gx8cuuxRQT46+wSbDGcd34q3n18Bu8or3OvVPuCKqSt/ziBtZvLyc51sptV0zUO5zz1ltTQ9N7\nG2jduhmv04khwkbcNdcSc8kSTNHRPruf3MxYvrlyCiaTgTCzvmWAbo+b5w+8RGFzCTMSp/AldWVQ\n/c4JcS7j06LJy4ojv6SRw0ebmTj2xHN6e5eTgvJmHCl24qNDb+rkaCOJjxBiVMrsT3zKzrLiczJF\nUbDPnEXkjAto+3QbDW+upuVfH9C6dTMxl1xG3NJlGCMjRzLk4xaPmcfu2n3sqz/Azpo9zAqikrcN\nnx0F4P4VeQHTtzIYXcVFNG14h/ZdO8HrxZSQQOzlVxK96EIMlpGp+Q+EKXder5eXtVXsrz+IGpvN\nVyd/BYMixSIi9CxfmEV+SSNrt5bw7S+feE7dW1iP2+OVTUtDRPC86gghhA/FR4UTaTWftdTtdBSD\ngaj5C7DPnkPL1i00vvUmTRveoXnjv7BNmdr3X94Un376f6q+kreb+PGnv+DVw2uYGJdNVFjgN5m7\n3B6OHGsmLcF2PPEMZF6Ph479+2ja8A5dhzUALBmZxF11NZEzZ6EYg3sgw2CsKXqHbVU7yLCnc++U\nOzAb5G2DCE3Z6dHkZsZyoLSJwooWssf0PYfv1GSMdSiRZzAhxKikKAqOVDv5xY20dznPe0M6xWQi\n5qKLiVqwgJaPNtL0/ru079hO+47tAFgyHccTofCscYOe6jVYCdZ4VmRfzT8Pr+EVbTX35N0e8OVH\npdVt9Do9qBn+Kw0cCo/TSdunn9C0YT29VZUARORN6RtYkJMb8NfZV94v/4j3yjeSHJHIg9O+TrhJ\nynxEaFuxKItDZU28ubWE/7h5Ol09LvJLGhmTaAvqISziBEl8hBCjliMlivziRkqrW8nLGloDucEc\nRuySK4i57HJ6q6ro2L+Xzvz9dB7W6CkrpfGtNzFERmKbnNeXCE2egtHum9WOC8fMZ3ftPvbW5bOz\ndi+zkqf75LwjRSvv2x099zRTkwKBq72DxnVv0fTB+7hbmsFoJGr+QmKvvApLun6jzPXwSdUOVhe+\nTYwlmm9Ovxt7mH/KOIXQ08SxMeRkxJBf3EhxZSv1LV243B4ZahBCJPERQoxaJ29kOtTEZ4CiKFjS\n0rCkpRF35VI83V10HjpIx/79dOzfR9un22j7dBsoCuFZWdimTMM2ZSqWjMwhrwYZFAO35dzMj7f/\nglcPv8HE2PEBXfJWUNaX+JzcOKw3d0cH3cVFdOTvp3DrZjzd3RjCw4m98ipiLrsCc9yZB1+Eqn11\nB3i54DVspgi+Of1u4sIDM1EVYiRcuzCLgvLdrN1acnywiJS5hQ5JfIQQo9bxxOc8+3wGwxBuJXLG\nTCJnzMTr9dJbcYyO/fvo2L+PrsIjdBcX07BmNUZ7FLa8KdimTCVich5Gm+287icxIp4V45fy2pE3\n+Ye2mrsDtOTN5fZwpKKFMQk2omxhusTg9Xpx1tTQVXiErqIjdBcV0ltZefz7YXFxRF2znOgLL8YY\nMTrLWo40FfP8gZcwKUYemHYnqbZkvUMSwq9yMmKYmB7N3qIGTEaFpBgr6Ynn97wsApckPkKIUSvW\nbiHKFkZpdeuI3o+iKFjSx2JJH0vc0mW4OzvoPHiwLxHK30frJ1tp/WRr32rQ+Gy6582GcSqWsRmD\nSmIuSl/A7tr97KnLZ1ftXmYGYMlbaZX/+3s8PT10l5XSXXiErqJCuooK8bS3H/++YrFgzcnFmp2N\ndfwEMhbPoaG522/xBZpjbZU8s+/PuL0e7p96J1nRmXqHJITfKYrCtYuy+Pkre3C5+6a5BeKHSWJo\nJPERQoxaiqLgSLGzr6iB1o5ev61EGCNs2GfNxj5rNl6Ph55jR+nYt5eO/fvoLiqkvPBI33HRMdim\n9K8G5U4+4yrEwJS3n2z/X/5x+A0mxmYHXE9GQX9/T84I9vc4m5roLjpCV2Eh3UWFdJeXgdt9/Pum\n+Hhsk/Owjs8mPHsCljHpn5vMZjCbgdGZ+NR1NvCbvX+ix93D1yZ9mcnxqt4hCaGbSZmxZI+JprCi\nRcrcQowkPkKIUW0g8SmtbmPq+OH1+QyFYjAQnpFJeEYm8dcsx93ejvFoEVUff0pn/n5at2ymdctm\nMBqxjs/u6w2aOpWwtDGf+xQyKSLh8yVvU273+2M5m4HBBr7q7/G63fQcO0pXUSHdhYV0FR7B1dhw\n4gCjkfDMTMLHT+hLdMZnY46VXpXTaelp5Td7nqWtt52bJq4Iqn2hhBgJiqJw3/LJFFW2MC4tSu9w\nhA9J4iOEGNUcKX0vaqXVrbokPqcyRkaSeOEiyJ3WtxpUVnqiN+jIYboOa9SvehVTbNzxcdkRuZMw\nhIf3l7ztY3fdfnbV7uOCpKl6PxzAN/09A0MIuvrL1rqLi/D29h7/vjHSjm36DKzjs7FmT8CS6cAQ\npk8vUTDpdHbx273PUd/dyFLHEi5OX6h3SEIEhPjocOKjZYR7qJHERwgxqg1spFk2AgMOhksxGAjP\nGkd41jjil1+Hq62Vzvz8vkTowH5aNm2kZdNGMBqJmKhimzKVr0y4iJ+2HuMf2momxIwLiJK38+3v\n6RtCUE1XYeFphxAAhKWNwZqd3beik52NOSlZ6vDPU6/byTP7/kxFexWLx8xnWdbleockhBAjShIf\nIcSoFmu3EB0ZNiKT3XzNZI8iav4CouYvwOvx0F1STMf+vXTs20fnoYN0HjoIwL0xkRxIdPNe85+4\nZu4tKGYzBrMZxWxGMZk+19fiD+fq7xnMEIKI3EmEj8/uS3ayxp/39DvxeW6Pm+cPvERRSwkXJE3l\n5okrJHEUQoQ8SXyEEKNeVkoUewrraWnvITrSonc4g6IYDH1lXeOzSbjuBlwtzXTk9+0Z1Hkgn2lH\nuuDIHkrX7PniDxsMfUlQfyJ0Iikyn3T7QLJkOuV20ymJ1EnfO/V8/d8vLygh0tVJdrwJT3c37s5O\nuosLzzyEICHhrEMIxPB4vV5eLljF/vqD5MRO4I5JX8agDG0vKSGECCaS+AghRj1Hip09hfWUVrcx\nLTs4Ep9TmaJjiF64mOiFi/G6XFQd2slHH/yN8E4nJrcXoweMbi8mtxeTRyHMo2D2uDF73Bi7ezB2\neDC4POByo3g8Po1tSf9/tQ+9Ru2p3zx5CEF2XyJnipEhBCNpddHbbKveQaZ9LPdMuQOzQd4KCCFG\nB3m2E0KMepknbWQ6LTtB52iGTzGZSJsyl6UTcyhrPUZrbystPW0097bS0tNKS28brT2ttPa248X7\nxZ/39CVKYR4DMUYb0cYIog0R2A1W7IZw7FiwKRYiFDMRhGHxGsHlwut04nW68Lqc/V87aWruYG9B\nNemxFjITrXidThSTifCs8Vizs2UIgZ+9V7aRD8o3kRyRxIPTvk64KTgTfSGEGApJfIQQo55jIPGp\nGtmNTP0txhJNTGL0Gb/v8Xpo6+3oT4xaae1to6Wn7fjfW3rbaOlppba3AZenFs6wEKSgYA+LJNpq\nJ8oSRXTYiT8Pl3TzSW8ct18ylaRJDkyyuqCbjys/442idcRYovnm9LuIDJM+KSHE6CKvQEKIUS86\n0kKs3RIUAw58yaAYiLbYibbYGWsfc8bjvF4vHa5OWnvaaOlt/cKfA0lSTWcdR9srv/Dz4ZPhn7Xb\n+Gct2MwRRIXZiTTbiAyL7PvTbCMyzIbdbCPSHElkmO347UaD9Pb4wt66A7xc8Bo2UwT/Nv1u4sKl\nnFAIMfpI4iOEEPSt+uw+Uk9TWw+xdin/OZmiKMcTkTRSznic1+ul291Da38i1NTdwp/f20NEpJsp\nauTxEruWnlaqOmoGdd9Wk7UvIQrrT4rMEackTP23mSOxh9kIM0rZ3KmONBXx/IGXMBvNPDDt66TY\nkvUOSQghdCGJjxBCcCLxKa1uJdaeqHc4QUlRFKymcKymcJJtSRw51kxPZTOLLkjn1skTP3es2+Om\n3dlJu7OdDmcHbb0dtDs7aO9t7/vT2UF7/21tznbqWxvxeM89dCHMYP7CSlKk2Ya9fyXJZrZhP76i\nFInVFB7SY5yPtlXwzL4X8Xq93DvlDrKiM/QOSQghdCOJjxBCAI7UKKBvs80ZEyTx8YWC8mYAcjK/\nuHGp0WA8XmY3GB6vhy5X90kJUTvtvR20OQe+7kuiBr5f1VGN0+M653kNioFIs42oMDuLs2YzM3Ym\nVlNo7NZe21nPb/c8R4+7hzsnf4Xc+Inn/iEhhAhhkvgIIQQnJruV1YyuPp+RVFDWt3HpxLFfTHzO\nl0ExYDNHYDNHkBxx7sTU6/XS63EeX0FqO81K0snJU01nLX/fv4Y3TO9ycfpCLhm7CJs5Ythx66Wl\np5Xf7PkTbc52vjTxOmYmT9c7JCGE0J0kPkIIAURFhBEfZaG0qhWv1xvS5U/+4HR5KKpoIT3Rhj3C\n/303iqJgMYZhscYRb4075/Fdri52Nu1ibcH7vFP6Ph8e3cSFYxZwWcaF2MMi/RCx73Q6u/jt3udo\n6G7k6qzLuTB9gd4hCSFEQJDERwgh+jlSoth5uI6mth7iokKj3EkvJVWt9Lo8qBnBMT3MarKyctJV\nzI6bzZaKbbxf/hHvlW9k47GtLEqby5LMi4ixnHk0eKDodffyzL4XqGiv4sIxC7jasUTvkIQQImBI\n4iOEEP0cqXZ2Hq6jtLpNEp9h0sr7ytxyMoZf5uZPFmMYl2VcyIVj5vNJ1We8W7aRfx3bwuaKT5iX\nNpsrMi4e1AqSHtweN8/lv0RRSykzk6Zx08TlsnIphBAnkcRHCCH6DfT5lFa3csFEGXAwHAODDXzR\n36MHs9HMhekLWJA2h+3Vu9lQ9iFbKrbxceV25iRfwBWOSwbVa+QvHq+HlwpeI7/hELlxE7lj0pcw\nKAa9wxJCiIAiiY8QQvRzpJyY7CaGTu/+Hl8yGUwsSJvN3JQL2FW7j/VlH7KtegefVu/kgqSpXOm4\nlDGRqbrG6PV6WV34Np9W78QRlcHdebdjMsjLuxBCnEqeGYUQol+k1UxCdDil1W0y4GAYgq2/ZzCM\nBiOzU2YwM3kae+sOsL70A3bW7mVn7V6mJUzmKsdlZESl6xLbe+Ub+fDoZlIiknhg2p2Em2QDXiGE\nOJ1BJT6qqs4Ffqpp2sWqqk4HngFcwGHgbk3TPKqqfhu4BfAAP9E0bbWqqlbgb0AS0AZ8VdO0OlVV\n5wG/6j/Hu5qmPeHzRyaEEEPgSLGzQ6ujobWbhGir3uEEpRP9PaGT+AwwKAZmJE1hemIeBxoKeKf0\nA/bWH2Bv/QEmxassdVzGuGiH3+L5uHI7a4reIdYSwzen302k2ea3+xZCiGBzzgJgVVW/B/wJGOj0\nfQx4UtO0RYAFWKaqagzwf4D5wBXAL/uPfQDYr2naYuAvwMP9tz9DX5K0CJirquoM3zwcIYQYnpM3\nMhVDM9DfowbZYIPzoSgKeQm5fGfmN/i36fcwIWYcBxs0fr7zd/xq1x/QGgvxer0jGsOeunxeLliF\nzRzBN6ffTWx46F5vIYTwhcGs+BQB1wN/7f/7biBOVVUFsANOoAMoA2z9/3n6j10E/Kz/63eAR1RV\njQIsmqYVAaiqugFY0n9eIYTQleP4gIM2ZuUk6RxN8HG6PBRWtJCeGEmk1ax3OCNOURRy4iaQEzeB\nwuYS1pd+wKHGwxzeU0RWVCZXOS5lcnyOz8smDzcV8cKBlzEbzTw47euk2OR3VQghzuWciY+maatU\nVXWcdNMR4Lf0rd60ABv7bz8KHASMwNP9t0X1HwN9pW7R/be1nnS+NmDcueKIjY3AZDKe6zC/Sky0\n6x3CqCPXXB+j6brPtFmAPVQ2dur6uIP1mh8obsDp8jAjJykoH8NwYk5MnMr8CVMpbCjl9UPr2VGx\nl9/ve4Gs2LHcMOlqZo2Z6pNJayVNR/nj/hfx4uV7i+5nakrusM+pp2D8PQkFct39T665/oYy3OBX\nwGJN0w6oqvoN4OfABiAVyOo/ZoOqqlvpS3AG/pXtQPMpt518+1k1NXUOIdSRk5hop65OSmH8Sa65\nPkbjdU+KsXKkvIna2lZdBhwE8zXftq8CgIwEW9A9Bl9d92jiuVO9lcvTLmFD2Yfsrt3P/2z9A6m2\nZK7KvJQLkqcNOQGq7azjFzt/T7erhzsn30KqMT3orvPJgvl3PZjJdfc/ueb+daYkcyjPvI2cWLGp\nBGKBJqAL6NE0rZu+RCYG2Apc3X/sUmCzpmmtQK+qquP7y+WuBDYPIQ4hhBgRjlQ7Hd0u6lq69Q4l\n6GijoL9nsNLtadyVdxsPz/02c1NmUtNZxwsH/85T2/6HTyo/w+1xn9f5mnta+M2eP9HmbOfmidcx\nM3naCEUuhBChaSgrPncDr6iq6gJ6gXs0TStVVXUJsE1VVQ+wBXiv/88XVVXd0n/sLf3nuB94ib6y\nuHc1Tft0mI9DCCF8xpESxfZDtZRWtZIUI5PdBmu09fcMVootiTsmfYmrs5bwbtlGtlXt4G8F/2Rd\n6ftckXkx81JmYTae/Xp1Ojv57Z7naOhuYlnW5VyYPt9P0QshROgYVOKjaVopMK//6y3AwtMc8xh9\nE99O1gncdJpjtw2cTwghAk1m/4CDsuo25uQm6xxN8CipasXp8pAjqz2nlWCN55acG1jquIz3yj/i\n48pPeUVbzTslH7Ak8yIWpc0lzPjFDV973b38ft+fqeyo5qL0BSx1LNEheiGECH6ygakQQpwiM/nE\nZDcxeAUD+/dkht7+Pb4UGx7DzRNXcGXmpXx4dBObKj5h1ZG1bCj9kMvGXsji9PlYTX07SLg9bp7L\n/xvFLaXMSp7OjROWy8a6QggxRJL4CCHEKSLCTSTHRVBa3YbX65U3moNUUNaEAkwcKys+gxFtsbMy\nexmXZ17MxqNb2HhsK2uK3+G98o1cPHYRF6UvYNWRteQ3FJAbN5Hbc2/2yVQ4IYQYrSTxEUKI03Ck\n2Pn0YA21zV0kx0boHU7Ac7rcFFW2kp4k/T3nK9Js45pxV3Lp2AvZVPExHx7dzLqS99hQ+iFurxtH\nVAb3TLkDk0FesoUQYjjkoyMhhDiN4xuZVkm522AUV/b198g0t6GLMFu5ynEZT85/iJXZy7CZIxgT\nmcoD0+7EcpreHyGEEOdHPj4SQojTOJ74VLcyd5IMODiXgTHWORnS3zNc4SYLSzIu4tKxiwGkvE0I\nIXxEEh8hhDiNjGQ7Cn2T3cS5FZRLf4+vScIjhBC+Jc+qQghxGlaLiZT4vgEHHq9X73ACmvT3CCGE\nCAaS+AghxBk4Uux097qpaezUO5SAJv09QgghgoEkPkIIcQaOlChA9vM5l4H+nlzp7xFCCBHAJPER\nQogzyOwfcCB9Pmc30N8zQfp7hBBCBDBJfIQQ4gwykiNRFCitatU7lIDldLkprGhlrPT3CCGECHCS\n+AghxBmEh5lIi7dRVtOOxyMDDk6nuLIVl9uDKmVuQgghApwkPkIIcRaZKXZ6nG6qZcDBaRUc379H\nytyEEEIENkl8hBDiLE7eyFR8kSb9PUIIIYKEJD5CCHEWjtT+yW5VMuDgVNLfI4QQIphI4iOEEGcx\nNql/wEGNJD6nkv4eIYQQwUQSHyGEOAuL2ciYBBvlNW24PR69wwko0t8jhBAimEjiI4QQ5+BIiaLX\n6aGqQQYcnGygv2eiJD5CCCGCgCQ+QghxDo7U/gEH0udz3PH+nuRIbOHS3yOEECLwSeIjhBDnkNk/\n2a2sWhKfAUUVff09OdLfI4QQIkhI4iOEEOcwNjESo0GRkdYnKShvAkCVMjchhBBBQhIfIYQ4h7CB\nAQe17TLgoJ9W3tzX3yP79wghhAgSkvgIIcQgZKbYcbo8VNbLgAOny01RpfT3CCGECC6S+AghxCCc\n2MhUyt2kv0cIIUQwksRHCCEGwdE/4KBUBhxIf48QQoigJImPEEIMQvrxAQeS+Az096jS3yOEECKI\nSOIjhBCDYDYZSE+M5GhtOy736B1w0Ot0U1TZQkaynQjp7xFCCBFEJPERQohBcqTacbk9VNR16B2K\nbooqW3G5vVLmJoQQIuhI4iOEEIN0os9n9A440Pr7e2SwgRBCiGAjiY8QQgySI6VvslvZKO7zKTi+\nf0+03qEIIYQQ50USHyGEGKQxiTZMRoWSUZr49DrdFEt/jxBCiCAliY8QQgySyWhgbFIkx2rbcbpG\n34AD6e8RQggRzCTxEUKI85CZEoXb46Wivl3vUPxO+nuEEEIEM0l8hBDiPBwfcFA1+srdCsqbURTp\n7xFCCBGcJPERQojzMFonu0l/jxBCiGAniY8QQpyHtAQbZpOB0lE24KCoogWX20uO9PcIIYQIUpL4\nCCHEeRgYcFBR14HT5dY7HL8pKG8GQJX+HiGEEEFKEh8hhDhPjhQ7bo+Xo7UdeofiN1p5U19/T7r0\n9wghhAhOkvgIIcR5GtjIdLT0+fQ43RRXtUp/jxBCiKAmiY8QQpynEwMORkefT7H09wghhAgBkvgI\nIcR5Sk2IIMxkGDUjraW/RwghRCiQxEcIIc6T0WAgI9lOZX0Hvc7QH3Bwor9HVnyEEEIEL9NgDlJV\ndS7wU03TLlZVdTrwDOACDgN3a5rmUVV1KfAYoAA7gW8A4cDfgCSgDfiqpml1qqrOA37Vf453NU17\nwsePSwghRlRmip3CihaO1rYzfkzoNvz3ON0UVbaSmWwnInxQLxlCCCFEQDrnio+qqt8D/kRfEgN9\nyc2TmqYtAizAMlVV7cB/A9domjYXKAUSgAeA/ZqmLQb+Ajzcf45ngFuARcBcVVVn+OwRCSGEH4yW\nPp+iihbcHi85UuYmhBAiyA2m1K0IuP6kv+8G4lRVVQA74AQWAPuBn6uquhmo0TStjr7EZn3/z70D\nLFFVNQqwaJpWpGmaF9gALPHJoxFCCD9xpPZPdqsK7cluJ/p7pMxNCCFEcDtn3YKmaatUVXWcdNMR\n4Lf0rd60ABuBG4BLgOlAO7BZVdVPgKj+Y6Cv1C26/7aT3ym0AePOFUdsbAQmk/Fch/lVYqJd7xBG\nHbnm+pDr/kVx8ZGEhxk5Vt8xItcnUK55cVUrBgXmT0/HZg39UdaBct1HE7nm+pDr7n9yzfU3lILt\nXwGLNU07oKrqN4CfA2uBzzRNqwZQVXUTfUlQK32rQvT/2XzKbSffflZNTZ1DCHXkJCbaqasL7RKX\nQCPXXB9y3c9sbFIkhRUtHKtoxhLmuw9mAuWa9zjdaGVNZCTb6WzvprO9HDgIDQAAEO1JREFUW++Q\nRlSgXPfRRK65PuS6+59cc/86U5I5lKlujZxYsakEYoFdQJ6qqgmqqpqAecBBYCtwdf+xS4HNmqa1\nAr2qqo7vL5e7Etg8hDiEEEJXjpQovF4orw3NFzPp7xFCCBFKhrLiczfwiqqqLqAXuEfTtFpVVR+i\nr18H4FVN0/JVVS0GXlRVdUv/sbf0f/9+4CXASN9Ut0+H9SiEEEIHjtT+AQdVbUwIwVHP0t8jhBAi\nlAwq8dE0rZS+VRw0TdsCLDzNMa8Ar5xyWydw02mO3TZwPiGECFahPtnt+P49YyXxEUIIEfxkA1Mh\nhBii5LgILGFGSqtDb7Jbj9NNcWUrjhQ7Vovs3yOEECL4SeIjhBBDZFAUHMl2qhs66epx6R2OTxX2\n9/eo0t8jhBAiREjiI4QQw5CZYscLHK1t1zsUn9LKmwDIkf4eIYQQIUISHyGEGIYTAw5Cq9ytoLwZ\nRSEkhzYIIYQYnSTxEUKIYchKiQJCa8BBT6+bEunvEUIIEWIk8RFCiGFIjLVitZgoCaHEp7BS+nuE\nEEKEHkl8hBBiGAyKQmZyJDWNoTPgQPp7hBBChCJJfIQQYpgcqX3lbsUh0udTUN6MQVGkv0cIIURI\nkcRHCCGGKS8rDoDVm4rxeLw6RzM8A/09mdLfI4QQIsRI4iOEEMM0yRHH3EnJFFe28u5nR/UOZ1gG\n9u+RMjchhBChRhIfIYTwgVuWTCAqwszrm4qpaujQO5whK+jv75HBBkIIIUKNJD5CCOED9ogwbrtC\nxeX28MK6gqAtedOO9/dE6x2KEEII4VOS+AghhI/Myklidk4ShRUtvL8j+EreenrdlFRJf48QQojQ\nJImPEEL40K1XTCTSambVpmJqGjv1Due8SH+PEEKIUCaJjxBC+FBURBi3XTERp8vD8+sO4fEGT8mb\n9PcIIYQIZZL4CCGEj83OSWKmmsiRYy18sPOY3uEMmvT3CCGECGWS+AghhI8pisJtV6h9JW8bi6ht\nCvySt4H+Hkeq9PcIIYQITZL4CCHECIi2hXHL5RPodfVPeQvwkrcjFc24PV5U6e8RQggRoiTxEUKI\nETI3N5kZExLQjjbzr10VeodzVlp5MwA50t8jhBAiREniI4QQI0RRFO64UsUWbuK1jUXUNXfpHdIZ\nFZQ3YVAUssdIf48QQojQJImPEEKMoOhIC7csmUiP080LATrlrbvXRWlVm/T3CCGECGmS+AghxAib\nNzmZ6dkJFJQ389GeSr3D+YKB/Xukv0cIIUQok8RHCCFGmKIo3H6lSoTFxKv/KqS+JbBK3qS/Rwgh\nxGggiY8QQvhBrN3CV5ZMoKfXzZ/fKcAbQCVvA/09sn+PEEKIUCaJjxBC+MmCvBSmjo/nYGkTm/YG\nRsnbQH9PVqqd8DDp7xFCCBG6JPERQgg/GZjyZrUY+ceHhTS0dOsdEoXHBvp7pMxNCCFEaJPERwgh\n/CguKpwvXzqB7l43L67Xv+St4Hh/jww2EEIIEdok8RFCCD9bNDWVvKw48ksa2bKvStdYtIH9e6S/\nRwghRIiTxEeI/9/evQfZWdYHHP/uJSSQG8mwARIgF8BfYtsBSpRAgEakgyRgHUvVYZAC0qrT9g/p\niCMDUjr+Y6fQKqMyUmm4mlYpU0YLZBAQgkItcgtlf7lwSUUDIeSykGSTJds/zrt4OGyWnc3uObvv\n+X7+es/7POc9zz555tn95f393iPVWUtLCxedPZ8JB7Sx4oG1vLG9MSlvu3b38KL1PZKkJmHgI0kN\nMH3KBD59xjHs7H6bW+7LhqS8rfv1Nvb2Wt8jSWoOBj6S1CCnHzeTD86ZxjPrN/Pz1Rvr/vnW90iS\nmomBjyQ1SF/K2/gD2vjB/WvZ0tVd18/PDVtoa7W+R5LUHAx8JKmBDpl6IJ/6yDHs6O7hljo+5a2v\nvmeO9T2SpCZh4CNJDfZHx89kwexpPL1+M48992pdPnNtUd8z3/oeSVKTMPCRpAZr7Ut5G9fGHfev\nqctT3jo3bAEgrO+RJDUJAx9JGgU6Dj6Q85YczVu7evjOj54e8ZS33LC1Ut8zy/oeSVJzMPCRpFHi\nI384izjyYB5/biOPPz9yKW87u3t4yfoeSVKTMfCRpFGitaWFi5dWnvJ2+8o1bHtr94h8zrpXrO+R\nJDUfAx9JGkVmTDuIC5cu4K1dPdw2Ql9san2PJKkZGfhI0ihzzuJ5fOCIqTyxZhO/7Hxt2K/fV99z\n7CwDH0lS8zDwkaRRprW1hYuXLmBceyu3rVzD9h3Dl/LWV98z9/ApjD+gbdiuK0nSaGfgI0mj0KHT\nD+JPT5/Hmzv3cPvKNcN23b7v7zHNTZLUbAb1OJ+IOAn4RmYuiYjjgRuAHmANcGlm7i36tQI/Af4z\nM2+IiAOB24AZQBfw55m5KSIWAd8srrEyM68Z7h9Mksa6Mxceyf9kJd3tQ52vsXD+jP2+Zhb1PT7Y\nQJLUbN73jk9EXA78CzChOHU18PeZeSowHlhW1f3rQPVv0y8Cz2bmacAtwJXF+RuA84FTgZMi4oT9\n+SEkqYwqKW/zGdfeyq0rk65hSHnr9Pt7JElNajB3fNYDnwRuLV4/CUyPiBZgMrAHICLOA/YC91a9\n91TgH4rje4CrImIKMD4z1xfvuw84s7juPk2bdhDt7aMrH72jY3Kjh9B0nPPGcN7rr2/OOzomc8HH\nFvCvP36OOx95kS9fsHDI19yxaw8vv9pFHDWNI3ywQb9c6/XnnDeG815/znnjvW/gk5l3RsScqlNr\ngW9TuXuzDXgoIn6fyh2c84CvVfWdUvSBSqrb1OLc9qo+XcC89xvHli073q9LXXV0TGbTpq5GD6Op\nOOeN4bzXX+2cL/7gDB7+1f/x8JOv8AdzpnNidAzpus+s38zevb3MO9x/0/641uvPOW8M573+nPP6\n2leQOZSHG3wTOC0z51NJX7sWuBCYBTwAXARcFhEfoxLg9H3yZGBrzbnq85KkfvQ95a29rZLy9ubO\nPUO6jvU9kqRmNpTA5w1+d8fmN8C0zLw8M0/KzCXAcuC6zLwXeBRYWvQ9G3gkM7cDuyPi6CJd7izg\nkf34GSSp9GYeMpFPnDaX7W/t5gf3D+0pb9b3SJKa2aCe6lbjUmBFRPQAu4G/GKDvd4GbI2JV0ff8\n4vwXgNuBNipPdXt8COOQpKZy1oeP5Il8jV889yoL58/ghGMHn/K2s7uHlzd2MW+W398jSWpOgwp8\nMvMlYFFxvApYPEDfv6s63gH8WT99Huu7niRpcNpaW7lk6QKuWf5Lbrkv+cCRBzNxwrhBvbfv+3vm\n+/09kqQm5ReYStIYMqtjEh9fPJdtb+5mxf1rB/2+zqK+J6zvkSQ1KQMfSRpjzl50FLMPm8yjqzfy\n9LrXB/We3LClUt8z0/oeSVJzMvCRpDGmrbWVzy1dQFtrCzff28mOXQM/5W1ndw8vbexi7kzreyRJ\nzcvAR5LGoCNmTOLcxXPY+uZuVjywbsC+a3+9ld5erO+RJDU1Ax9JGqOWLprNUTMmseqZ3/LsC5v3\n2a9zQ+Wr0qzvkSQ1MwMfSRqj2ttauWRZJeVt+T2d7NjV02+/d+p7/P4eSVITM/CRpDHsqEMns+zk\n2Wzp6ubfH3xvyltffc+8mVMYP876HklS8zLwkaQx7pxT5nBExyQefvo3rH7x3SlvffU9prlJkpqd\ngY8kjXHtba18btkCWltauPmeTnZ2/y7lrfPlSn2PDzaQJDU7Ax9JKoHZh01m6cmz2by9mx8+tP6d\n851Ffc/R1vdIkpqcgY8klcS5p8xhVsdEHnryFf73pTfYsauHl1+1vkeSJDDwkaTSGNfeyiVLKylv\ny+/pZPWLm63vkSSpYOAjSSUy9/ApnL3oKF7ftoub7+0ErO+RJAkMfCSpdD6+eC4zD5nIzu63aW+z\nvkeSJDDwkaTS6Ut5a2mBY2ZNtb5HkiSgvdEDkCQNv3kzp3DlhQuZOvGARg9FkqRRwcBHkkpq7uFT\nGj0ESZJGDVPdJEmSJJWegY8kSZKk0jPwkSRJklR6Bj6SJEmSSs/AR5IkSVLpGfhIkiRJKj0DH0mS\nJEmlZ+AjSZIkqfQMfCRJkiSVnoGPJEmSpNIz8JEkSZJUegY+kiRJkkrPwEeSJElS6Rn4SJIkSSo9\nAx9JkiRJpdfS29vb6DFIkiRJ0ojyjo8kSZKk0jPwkSRJklR6Bj6SJEmSSs/AR5IkSVLpGfhIkiRJ\nKj0DH0mSJEmlZ+AjSZIkqfTaGz2A0SwiWoHvAMcB3cClmbmuqv1c4GtAD3BTZt7YkIGWTESMA24C\n5gDjga9n5t1V7V8CLgU2Fac+n5lZ73GWTUT8CthevHwxMy+uanOtj4CIuAi4qHg5ATgeOCwztxbt\nrvVhFBEnAd/IzCURcQywHOgFVgN/lZl7q/oOuP9rcGrm/HjgeuBtKnN6YWa+WtN/n/uQBq9m3k8A\nfgysLZq/m5n/VtXXtT4MauZ8BXBY0TQHeCwzP1PT37XeAAY+A/sEMCEzT46IRcC1wJ/AO3+c/xPw\nIeAt4NGIuLt2E9eQXABszszPRsR04Cng7qr2E6n8wnyiIaMroYiYALRk5pJ+2lzrIyQzl1P545uI\n+DaVoHJrVRfX+jCJiMuBz1JZwwDXAVdm5kMRcQOVvf2uqrfsc//X4PQz598E/iYzn4qIzwNfAS6r\n6r/PfUiD18+8nwhcl5nX7uMtrvX9VDvnfUFOREwDHgS+VNPftd4gproN7FTgXoDMfAxYWNW2AFiX\nmVsyczewCji9/kMspR8CVxXHLVTuMlQ7EfhqRKyKiK/WdWTldRxwUESsjIgHil9+fVzrIywiFgK/\nl5nfq2lyrQ+f9cAnq16fCPysOL4HOLOm/0D7vwands4/k5lPFcftwK6a/gPtQxq8/tb6soh4OCK+\nHxGTa/q71vdf7Zz3uQa4PjN/W3Petd4gBj4DmwJsq3r9dkS076OtC5har4GVWWa+mZldxeb8I+DK\nmi4rgC8AZwCnRsQ59R5jCe0A/hE4i8rc3u5ar6srqPyCrOVaHyaZeSewp+pUS2b2Fsf9remB9n8N\nQu2c9/3xFxGnAH9N5U5ytYH2IQ1SP2v9v4EvZ+bpwAvA1TVvca3vp37mnIiYAXyU4q5+Ddd6gxj4\nDGw7UP0/I62Z2bOPtslAdYqK9kNEHEnl9vCtmXlH1fkW4J8z8/Xi7sNPgBMaNMwyWQPclpm9mbkG\n2AwcXrS51kdQRBwMRGY+WHPetT6y9lYd97emB9r/NUQR8WngBmBZZm6qaR5oH9LQ3VWVLnsX791H\nXOsj4zzgjsx8u58213qDGPgM7FFgKUBxG/LZqrbngWMjYnpEHEAl9ecX9R9i+UTEocBK4CuZeVNN\n8xRgdURMKv4wPAOw/mH/XUIlr5uImEllnvtuzbvWR9bpwE/7Oe9aH1lPRsSS4vhs4JGa9oH2fw1B\nRFxA5U7Pksx8oZ8uA+1DGrr7IuLDxfFHee8+4lofGWdSSaPtj2u9QbytNrC7gD+OiJ9TqTW5OCLO\nByZl5vci4jLgPioB5E2Z+UoDx1omVwDTgKsioq/W50ZgYjHvV1C5G9QN/DQz/6tB4yyT7wPLI2IV\nladcXQJ8KiJc6yMvqKSfVF68e49xrY+cvwVuLIL556mk1RIRt1BJr33P/t+ogZZBRLQB3wI2AP8R\nEQA/y8yrq+b8PfuQdx6GxReB6yNiD7AR+EtwrdfBu/Z2eNecu9YbpKW3t/f9e0mSJEnSGGaqmyRJ\nkqTSM/CRJEmSVHoGPpIkSZJKz8BHkiRJUukZ+EiSJEkqPQMfSZIkSaVn4CNJkiSp9P4fbDSwmo/p\nt+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4db080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "metrics_func rolling_mean=267.2156304197113, metrics_func y_fit=250.7485519077053\n",
      "least poly = 4, zoom=False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsi = get_price('hkHSI', start_date='20160128', end_date='20160229')\n",
    "tl.AbuTLine(hsi.price, 'HSI').show_least_valid_poly()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上结果显示至少需要最少4次以上的拟合才可以代表原始曲线，实际上poly的次数越大，越代表此段周期内波动比较大，从上可视化图也可清晰看出，上面需要4次拟合才能代表的走势明显处在震荡走势中。\n",
    "\n",
    "下面根据上面这个判断依据编写策略基本，策略和之前一直使用的AbuFactorBuyBreak基本一样，不同的是实现fit_month方法(fit_month即在回测策略中每一个月执行一次的方法), 在fit_month中使用show_least_valid_poly计算这个月大盘价格走势的least poly值，如果大于一个阀值比如2，就说明最近大盘走势震荡，那么就封锁交易，直到在fit_month中计算出的least poly值小于阀值，解锁交易，代码实现如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuFactorBuyXD, BuyCallMixin\n",
    "\n",
    "class AbuSDBreak(AbuFactorBuyXD, BuyCallMixin):\n",
    "    def _init_self(self, **kwargs):\n",
    "        super(AbuSDBreak, self)._init_self(**kwargs)\n",
    "        # 外部可以设置poly阀值，self.poly在fit_month中和每一个月大盘计算的poly比较，若是大盘的poly大于poly认为走势震荡\n",
    "        self.poly = kwargs.pop('poly', 2)\n",
    "        # 是否封锁买入策略进行择时交易\n",
    "        self.lock = False\n",
    "\n",
    "    def fit_month(self, today):\n",
    "        # fit_month即在回测策略中每一个月执行一次的方法\n",
    "        # 策略中拥有self.benchmark，即交易基准对象，AbuBenchmark实例对象，benchmark.kl_pd即对应的市场大盘走势\n",
    "        benchmark_df = self.benchmark.kl_pd\n",
    "        # 拿出大盘的今天\n",
    "        benchmark_today = benchmark_df[benchmark_df.date == today.date]\n",
    "        if benchmark_today.empty:\n",
    "            return 0\n",
    "        # 要拿大盘最近一个月的走势，准备切片的start，end\n",
    "        end_key = int(benchmark_today.iloc[0].key)\n",
    "        start_key = end_key - 20\n",
    "        if start_key < 0:\n",
    "            return False\n",
    "\n",
    "        # 使用切片切出从今天开始向前20天的数据\n",
    "        benchmark_month = benchmark_df[start_key:end_key + 1]\n",
    "        # 通过大盘最近一个月的收盘价格做为参数构造AbuTLine对象\n",
    "        benchmark_month_line = AbuTLine(benchmark_month.close, 'benchmark month line')\n",
    "        # 计算这个月最少需要几次拟合才能代表走势曲线\n",
    "        least = benchmark_month_line.show_least_valid_poly(show=False)\n",
    "\n",
    "        if least >= self.poly:\n",
    "            # 如果最少的拟合次数大于阀值self.poly，说明走势成立，大盘非震荡走势，解锁交易\n",
    "            self.lock = False\n",
    "        else:\n",
    "            # 如果最少的拟合次数小于阀值self.poly，说明大盘处于震荡走势，封锁策略进行交易\n",
    "            self.lock = True\n",
    "\n",
    "    def fit_day(self, today):\n",
    "        if self.lock:\n",
    "            # 如果封锁策略进行交易的情况下，策略不进行择时\n",
    "            return None\n",
    "\n",
    "        # 今天的收盘价格达到xd天内最高价格则符合买入条件\n",
    "        if today.close == self.xd_kl.close.max():\n",
    "            return self.buy_tomorrow()\n",
    "    \n",
    "# 通过import的方式导入AbuSDBreak，因为在windows系统上，启动并行后，在ipython notebook中定义的类会在子进程中无法找到\n",
    "from abupy import AbuSDBreak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面重新组成买入因子字典，使用刚刚编写的买入策略AbuSDBreak，突破周期还是60，42："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "buy_factors = [{'xd': 60, 'class': AbuSDBreak}, \n",
    "               {'xd': 42, 'class': AbuSDBreak}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用run_loop_back运行策略\n",
    "abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   choice_symbols=choice_symbols,\n",
    "                                                   n_folds=6)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:122\n",
      "买入后尚未卖出的交易数量:3\n",
      "胜率:61.4754%\n",
      "平均获利期望:11.5062%\n",
      "平均亏损期望:-4.7367%\n",
      "盈亏比:3.9707\n",
      "策略收益: 91.2822%\n",
      "基准收益: 18.5928%\n",
      "策略年化收益: 15.6271%\n",
      "基准年化收益: 3.1830%\n",
      "策略买入成交比例:98.4000%\n",
      "策略资金利用率比例:22.8984%\n",
      "策略共执行1472个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiIiPabcVYkv6A/vz8o8uc35YT08t6aZhqKJiIiIiEiPKWoqHNDM1M0FN4+VEhsRERER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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f55a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x11b728b70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直观上的收益曲线变的平缓了些，交易数量减少了接近40％。\n",
    "\n",
    "使用AbuSDBreak，降低交易频率，提高系统的稳定性处理的回测结果：\n",
    "\n",
    "- 买入后卖出的交易数量:123\n",
    "- 胜率:60.9756%\n",
    "- 平均获利期望:12.3589%\n",
    "- 平均亏损期望:-4.6619%\n",
    "- 盈亏比:4.2144\n",
    "- 策略收益: 100.1270%\n",
    "- 基准收益: 18.5928%\n",
    "- 策略年化收益: 17.1413%\n",
    "- 基准年化收益: 3.1830%\n",
    "- 策略买入成交比例:98.4127%\n",
    "- 策略资金利用率比例:22.3742%\n",
    "- 策略共执行1472个交易日\n",
    "\n",
    "本节时使用AbuFactorBuyBreak的回测结果：\n",
    "\n",
    "- 买入后卖出的交易数量:201\n",
    "- 胜率:50.7463%\n",
    "- 平均获利期望:13.2619%\n",
    "- 平均亏损期望:-5.9121%\n",
    "- 盈亏比:2.6158\n",
    "- 策略收益: 93.7512%\n",
    "- 基准收益: 18.5928%\n",
    "- 策略年化收益: 16.0498%\n",
    "- 基准年化收益: 3.1830%\n",
    "- 策略买入成交比例:91.1765%\n",
    "- 策略资金利用率比例:32.3410%\n",
    "- 策略共执行1472个交易日"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面这种提高系统稳定性的方法需要在策略添加一些交易规则，阀值进行交易的筛选过滤，后面的章节会示例使用abupy中ump裁判拦截系统，其优点是：\n",
    "\n",
    "1. 不需要在具体策略中硬编码\n",
    "2. 不需要人工设定阀值，即且使得代码逻辑清晰\n",
    "3. 分离基础策略和策略优化监督模块，提高灵活度和适配性\n",
    "4. 发现策略中隐藏的交易策略问题\n",
    "5. 可以通过不断的学习新的交易数据（给每一个裁判看更多的比赛录像，提高比赛录像水准），提高拦截水平"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读者可自行使用上面的AbuSDBreak做为上一节A股回测的策略，重新进行回测，从结果你可以发现与本节港股的回测类似，交易频率降低，交易成功概率提高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 将优化策略的'策略'做为类装饰器进行封装\n",
    "\n",
    "在上面说过使用ump模块的优点有个是：分离基础策略和策略优化监督模块，提高灵活度和适配性，\n",
    "\n",
    "实际上上面使用的AbuSDBreak中的优化策略可以封装在一个装饰器类中，之后编写的所有策略都可以选择是否通过这个类装饰器进行策略优化，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AbuLeastPolyWrap(object):\n",
    "    \"\"\"\n",
    "        做为买入因子策略装饰器封装show_least_valid_poly对大盘震荡大的情况下封锁交易\n",
    "    \"\"\"\n",
    "\n",
    "    def __call__(self, cls):\n",
    "        \"\"\"只做为买入因子策略类的装饰器\"\"\"\n",
    "        \n",
    "        if isinstance(cls, six.class_types):\n",
    "            # 只做为类装饰器使用\n",
    "            \n",
    "            init_self = cls._init_self\n",
    "            org_fit_day = cls.fit_day\n",
    "            \n",
    "            # fit_month不是必须实现的\n",
    "            org_fit_month = getattr(cls, 'fit_month', None)\n",
    "            def init_self_wrapped(*args, **kwargs):\n",
    "                # 拿出被装饰的self对象\n",
    "                warp_self = args[0]\n",
    "                # 外部可以设置poly阀值，self.poly在fit_month中和每一个月大盘计算的poly比较，\n",
    "                # 若是大盘的poly大于poly认为走势震荡\n",
    "                warp_self.poly = kwargs.pop('poly', 2)\n",
    "                # 是否封锁买入策略进行择时交易\n",
    "                warp_self.lock = False\n",
    "                # 调用原始的_init_self\n",
    "                init_self(*args, **kwargs)\n",
    "                    \n",
    "            def fit_day_wrapped(*args, **kwargs):\n",
    "                # 拿出被装饰的self对象\n",
    "                warp_self = args[0]\n",
    "                if warp_self.lock:\n",
    "                    # 如果封锁策略进行交易的情况下，策略不进行择时\n",
    "                    return None\n",
    "                return org_fit_day(*args, **kwargs)\n",
    "                                \n",
    "            def fit_month_wrapped(*args, **kwargs):\n",
    "                warp_self = args[0]\n",
    "                today = args[1]\n",
    "                # fit_month即在回测策略中每一个月执行一次的方法\n",
    "                # 策略中拥有self.benchmark，即交易基准对象，AbuBenchmark实例对象，benchmark.kl_pd即对应的市场大盘走势\n",
    "                benchmark_df = warp_self.benchmark.kl_pd\n",
    "                # 拿出大盘的今天\n",
    "                benchmark_today = benchmark_df[benchmark_df.date == today.date]\n",
    "                if benchmark_today.empty:\n",
    "                    return 0\n",
    "                # 要拿大盘最近一个月的走势，准备切片的start，end\n",
    "                end_key = int(benchmark_today.iloc[0].key)\n",
    "                start_key = end_key - 20\n",
    "                if start_key < 0:\n",
    "                    return 0\n",
    "\n",
    "                # 使用切片切出从今天开始向前20天的数据\n",
    "                benchmark_month = benchmark_df[start_key:end_key+1]\n",
    "                # 通过大盘最近一个月的收盘价格做为参数构造AbuTLine对象\n",
    "                benchmark_month_line = tl.AbuTLine(benchmark_month.close, 'benchmark month line')\n",
    "                # 计算这个月最少需要几次拟合才能代表走势曲线\n",
    "                least = benchmark_month_line.show_least_valid_poly(show=False)\n",
    "\n",
    "                if least >= warp_self.poly:\n",
    "                    # 如果最少的拟合次数大于阀值self.poly，说明走势成立，大盘非震荡走势，解锁交易\n",
    "                    warp_self.lock=False\n",
    "                else:\n",
    "                    # 如果最少的拟合次数小于阀值self.poly，说明大盘处于震荡走势，封锁策略进行交易\n",
    "                    warp_self.lock=True\n",
    "                    \n",
    "                if org_fit_month is not None:\n",
    "                    return org_fit_month(*args, **kwargs)\n",
    "\n",
    "            cls._init_self = init_self_wrapped\n",
    "            init_self_wrapped.__name__ = '_init_self'\n",
    "                                \n",
    "            cls.fit_day = fit_day_wrapped\n",
    "            fit_day_wrapped.__name__ = 'fit_day'\n",
    "            \n",
    "            cls.fit_month = fit_month_wrapped\n",
    "            fit_month_wrapped.__name__ = 'fit_month'\n",
    "\n",
    "            return cls\n",
    "        else:\n",
    "            raise TypeError('AbuLeastPolyWrap just for class warp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "下面编写一个很简单的策略，连续涨两天就买入股票，示例AbuLeastPolyWrap的使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuFactorBuyTD, BuyCallMixin\n",
    "\n",
    "@AbuLeastPolyWrap()\n",
    "class AbuTwoDayBuy(AbuFactorBuyTD, BuyCallMixin):\n",
    "    \"\"\"示例AbuLeastPolyWrap，混入BuyCallMixin，即向上突破触发买入event\"\"\"\n",
    "\n",
    "    def _init_self(self, **kwargs):\n",
    "        \"\"\"简单示例什么都不编写了\"\"\"\n",
    "        pass\n",
    "\n",
    "    def fit_day(self, today):\n",
    "        \"\"\"\n",
    "        针对每一个交易日拟合买入交易策略，今天涨，昨天涨就买\n",
    "        :param today: 当前驱动的交易日金融时间序列数据\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        # 今天的涨幅\n",
    "        td_change = today.p_change\n",
    "        # 昨天的涨幅\n",
    "        yd_change = self.yesterday.p_change\n",
    "\n",
    "        if td_change > 0 and 0 < yd_change < td_change:\n",
    "            # 连续涨两天, 且今天的涨幅比昨天还高 －>买入, 用到了今天的涨幅，只能明天买\n",
    "            return self.buy_tomorrow()\n",
    "        return None\n",
    "\n",
    "# 下面再次通过import的方式导入了AbuTwoDayBuy，因为在windows系统上，启动并行后，在ipython notebook中定义的类会在子进程中无法找到\n",
    "from abupy import AbuTwoDayBuy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下通过字典装载买入因子，只使用AbuTwoDayBuy一个买入因子，字典中设定poly=2, 即多于2次拟合的趋势都认为大盘是处于震荡走势，封锁交易行为，因为基础策略AbuTwoDayBuy中的实现属于频繁买入策略，所以AbuLeastPolyWrap装饰器装饰这个策略的目的就是控制降低交易频率，提过稳定性。\n",
    "\n",
    "备注: 读者可尝试切换poly值1-4来进行回测，查看回测效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "buy_factors = [{'class': AbuTwoDayBuy, 'poly': 2}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后依然使用abu.run_loop_back对交易进行回测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abu_result_tuple, kl_pd_manger = abu.run_loop_back(read_cash,\n",
    "                                                   buy_factors,\n",
    "                                                   sell_factors,\n",
    "                                                   choice_symbols=choice_symbols,\n",
    "                                                   n_folds=6)\n",
    "ABuProgress.clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:598\n",
      "买入后尚未卖出的交易数量:13\n",
      "胜率:47.6589%\n",
      "平均获利期望:12.2090%\n",
      "平均亏损期望:-7.2464%\n",
      "盈亏比:1.7095\n",
      "策略收益: 108.1026%\n",
      "基准收益: 18.5928%\n",
      "策略年化收益: 18.5067%\n",
      "基准年化收益: 3.1830%\n",
      "策略买入成交比例:55.3191%\n",
      "策略资金利用率比例:49.3860%\n",
      "策略共执行1472个交易日\n"
     ]
    },
    {
     "data": {
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NBaMhPP2stwUE3AE3wVCQdIutx9ceVmDjcDj2AsdFbv8r7vFXgFcOZ9siIiIiIjL8+YJ+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\n1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spLGwoDOvYMR3Kckz9+5zRuf/DDQbUwrWvq4JdPfUa008bVZ0/l7c/0lIOe7VKjI+yc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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b801f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<abupy.MetricsBu.ABuMetricsBase.AbuMetricsBase at 0x11f547f60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AbuMetricsBase.show_general(*abu_result_tuple, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读者可以看到AbuTwoDayBuy这个策略有多么简单甚至简陋，但是配合AbuLeastPolyWrap进行回测的效果也还可以。\n",
    "\n",
    "有很多做量化的人声称自己的策略多么复杂，多么nb，里面有多少高科技，但是就是不能对外公布，公布了就没有效果了，还请你原谅它。\n",
    "\n",
    "在我看来简单的基础策略是最好的，简单策略和复杂策略输出的都只是什么时候买，什么时候卖。\n",
    "\n",
    "基础策略追求的就应该是简单（可以一句话说明你的基础策略），不要刻意追求复杂的策略，对量化策略失败结果的人工分析，通过外层辅助优化策略，指导策略，让策略自我进行学习，自我调整才是应该追求复杂的地方。\n",
    "\n",
    "在《量化交易之路》中反复强调：投资的目标需要有一个比较准确的预测结果，投机的目标是获取交易概率优势，量化交易更倾向于投机范畴，预测肯定了确定性，概率优势不需要肯定确定性，对确定性认识的差异导致交易者最终的交易行为产生根本区别，复杂的策略和简单的策略都对应着概率优势，但是对于策略本身复杂的策略并不一定比简单的策略有效。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于港股交易还有一个相对其它市场特殊的地方，即每手的数量，A股市场每手的数量是定值100，美股一股也可以买卖，港股是每一个交易品种都有自己对应的每手数量，下面使用AbuHkUnit对每手数量进行查询："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hk00656': 500,\n",
       " 'hk00700': 100,\n",
       " 'hk01359': 1000,\n",
       " 'hk02318': 500,\n",
       " 'hk02333': 500,\n",
       " 'hk03333': 1000,\n",
       " 'hk03888': 1000}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit = AbuHkUnit()\n",
    "{symbol: unit.query_unit(symbol) for symbol in choice_symbols}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到每一个股票的unit都是不同的，在abupy内部会根据资金管理计算出来的买入数量和对应股票的unit进行对比，不满足一手的不能成交，其它市场的实现也类似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### abu量化文档目录章节\n",
    "\n",
    "1. [择时策略的开发](http://www.abuquant.com/lecture/lecture_1.html)\n",
    "2. [择时策略的优化](http://www.abuquant.com/lecture/lecture_2.html)\n",
    "3. [滑点策略与交易手续费](http://www.abuquant.com/lecture/lecture_3.html)\n",
    "4. [多支股票择时回测与仓位管理](http://www.abuquant.com/lecture/lecture_4.html)\n",
    "5. [选股策略的开发](http://www.abuquant.com/lecture/lecture_5.html)\n",
    "6. [回测结果的度量](http://www.abuquant.com/lecture/lecture_6.html)\n",
    "7. [寻找策略最优参数和评分](http://www.abuquant.com/lecture/lecture_7.html)\n",
    "8. [A股市场的回测](http://www.abuquant.com/lecture/lecture_8.html)\n",
    "9. [港股市场的回测](http://www.abuquant.com/lecture/lecture_9.html)\n",
    "10. [比特币，莱特币的回测](http://www.abuquant.com/lecture/lecture_10.html)\n",
    "11. [期货市场的回测](http://www.abuquant.com/lecture/lecture_11.html)\n",
    "12. [机器学习与比特币示例](http://www.abuquant.com/lecture/lecture_12.html)\n",
    "13. [量化技术分析应用](http://www.abuquant.com/lecture/lecture_13.html)\n",
    "14. [量化相关性分析应用](http://www.abuquant.com/lecture/lecture_14.html)\n",
    "15. [量化交易和搜索引擎](http://www.abuquant.com/lecture/lecture_15.html)\n",
    "16. [UMP主裁交易决策](http://www.abuquant.com/lecture/lecture_16.html)\n",
    "17. [UMP边裁交易决策](http://www.abuquant.com/lecture/lecture_17.html)\n",
    "18. [自定义裁判决策交易](http://www.abuquant.com/lecture/lecture_18.html)\n",
    "19. [数据源](http://www.abuquant.com/lecture/lecture_19.html)\n",
    "20. [A股全市场回测](http://www.abuquant.com/lecture/lecture_20.html)\n",
    "21. [A股UMP决策](http://www.abuquant.com/lecture/lecture_21.html)\n",
    "22. [美股全市场回测](http://www.abuquant.com/lecture/lecture_22.html)\n",
    "23. [美股UMP决策](http://www.abuquant.com/lecture/lecture_23.html)\n",
    "\n",
    "abu量化系统文档教程持续更新中，请关注公众号中的更新提醒。\n",
    "\n",
    "#### 《量化交易之路》目录章节及随书代码地址\n",
    "\n",
    "1. [第二章 量化语言——Python](https://github.com/bbfamily/abu/tree/master/ipython/第二章-量化语言——Python.ipynb)\n",
    "2. [第三章 量化工具——NumPy](https://github.com/bbfamily/abu/tree/master/ipython/第三章-量化工具——NumPy.ipynb)\n",
    "3. [第四章 量化工具——pandas](https://github.com/bbfamily/abu/tree/master/ipython/第四章-量化工具——pandas.ipynb)\n",
    "4. [第五章 量化工具——可视化](https://github.com/bbfamily/abu/tree/master/ipython/第五章-量化工具——可视化.ipynb)\n",
    "5. [第六章 量化工具——数学：你一生的追求到底能带来多少幸福](https://github.com/bbfamily/abu/tree/master/ipython/第六章-量化工具——数学.ipynb)\n",
    "6. [第七章 量化系统——入门：三只小猪股票投资的故事](https://github.com/bbfamily/abu/tree/master/ipython/第七章-量化系统——入门.ipynb)\n",
    "7. [第八章 量化系统——开发](https://github.com/bbfamily/abu/tree/master/ipython/第八章-量化系统——开发.ipynb)\n",
    "8. [第九章 量化系统——度量与优化](https://github.com/bbfamily/abu/tree/master/ipython/第九章-量化系统——度量与优化.ipynb)\n",
    "9. [第十章 量化系统——机器学习•猪老三](https://github.com/bbfamily/abu/tree/master/ipython/第十章-量化系统——机器学习•猪老三.ipynb)\n",
    "10. [第十一章 量化系统——机器学习•ABU](https://github.com/bbfamily/abu/tree/master/ipython/第十一章-量化系统——机器学习•ABU.ipynb)\n",
    "11. [附录A 量化环境部署](https://github.com/bbfamily/abu/tree/master/ipython/附录A-量化环境部署.ipynb)\n",
    "12. [附录B 量化相关性分析](https://github.com/bbfamily/abu/tree/master/ipython/附录B-量化相关性分析.ipynb)\n",
    "13. [附录C 量化统计分析及指标应用](https://github.com/bbfamily/abu/tree/master/ipython/附录C-量化统计分析及指标应用.ipynb)\n",
    "\n",
    "[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n",
    "\n",
    "\n",
    "更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n",
    "\n",
    "更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n",
    "\n",
    "更多关于abu量化系统请关注微信公众号: abu_quant\n",
    "\n",
    "如有任何问题也可在公众号中联系我的个人微信号。\n",
    "\n",
    "![](./image/qrcode.jpg)"
   ]
  }
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